52 Weeks of Cloud: Recent Episodes
Noah Gift
A weekly podcast on technical topics related to cloud computing including: AWS, Azure, GCP, Multi-Cloud and Kubernetes.
Key Argument Thesis: Using ELO for AI agent evaluation = measuring noise * Problem: Wrong evaluators, wrong metrics, wrong assumptions * Solution*: Quantitative assessment frameworks
The Comparison (00:00-02:00)Chess ELO
- FIDE arbiters: 120hr training
- Binary outcome: win/loss
- Test-retest: r=0.95
- Cohen's κ=0.92
AI Agent ELO
- Random users: Google engineer? CS student? 10-year-old?
- Undefined dimensions: accuracy? style? speed?
- Test-retest: r=0.31 (coin flip)
- Cohen's κ=0.42
Cognitive Bias Cascade (02:00-03:30) Anchoring: 34% rating variance in first 3 seconds * Confirmation: 78% selective attention to preferred features * Dunning-Kruger: d=1.24 effect size * Result*: Circular preferences (A>B>C>A)
The Quantitative Alternative (03:30-05:00)Objective Metrics
- McCabe complexity ≤20
- Test coverage ≥80%
- Big O notation comparison
- Self-admitted technical debt
- Reliability: r=0.91 vs r=0.42
- Effect size: d=2.18
Dream Scenario vs Reality (05:00-06:00)Dream
- World's best engineers
- Annotated metrics
- Standardized criteria
Reality
- Random internet users
- No expertise verification
- Subjective preferences
Key Statistics
| Metric | Chess | AI Agents | | --- | --- | --- | | Inter-rater reliability | κ=0.92 | κ=0.42 | | Test-retest | r=0.95 | r=0.31 | | Temporal drift | ±10 pts | ±150 pts | | Hurst exponent | 0.89 | 0.31 |
Takeaways1. Stop: Using preference votes as quality metrics 2. Start: Automated complexity analysis 3. ROI: 4.7 months to break even
Citations Mentioned* Kapoor et al. (2025): "AI agents that matter" - κ=0.42 finding * Santos et al. (2022): Technical Debt Grading validation * Regan & Haworth (2011): Chess arbiter reliability κ=0.92 * Chapman & Johnson (2002): 34% anchoring effect
Quotable Moments
"You can't rate chess with basketball fans"
"0.31 reliability? That's a coin flip with extra steps"
"Every preference vote is a data crime"
"The psychometrics are screaming"
Resources* Technical Debt Grading (TDG) Framework * PMAT (Pragmatic AI Labs MCP Agent Toolkit) * McCabe Complexity Calculator * Cohen's Kappa Calculator
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AI coding agents face the same fundamental limitation as parallel computing: Amdahl's Law. Just as 10 cooks can't make soup 10x faster, 10 AI agents can't code 10x faster due to inherent sequential bottlenecks.
📚 Key ConceptsThe Soup Analogy* Multiple cooks can divide tasks (prep, boiling water, etc.) * But certain steps MUST be sequential (can't stir before ingredients are in) * Adding more cooks hits diminishing returns quickly * Perfect metaphor for parallel processing limits
Amdahl's Law Explained* Mathematical principle: Speedup = 1 / (Sequential% + Parallel%/N)
* Logarithmic relationship = rapid plateau
* Sequential work becomes the hard ceiling
* Even infinite workers can't overcome sequential bottlenecks
💻 Traditional Computing Bottlenecks I/O Operations - disk reads/writes * Network calls - API requests, database queries * Database locks - transaction serialization * CPU waiting* - can't parallelize waiting * Result: 16 cores ≠ 16x speedup in real world
🤖 Agentic Coding Reality: The New Bottlenecks1. Human Review (The New I/O)* Code must be understood by humans * Security validation required * Business logic verification * Can't parallelize human cognition
2. Production Deployment* Sequential by nature * One deployment at a time * Rollback requirements * Compliance checks
3. Trust Building* Can't parallelize reputation * Bad code = deleted customer data * Revenue impact risks * Trust accumulates sequentially
4. Context Limits* Human cognitive bandwidth * Understanding 100k+ lines of code * Mental model limitations * Communication overhead
📊 The Numbers (Theoretical Speedups) 1 agent: 1.0x (baseline) * 2 agents: ~1.3x speedup * 10 agents: ~1.8x speedup * 100 agents: ~1.96x speedup * ∞ agents*: ~2.0x speedup (theoretical maximum)
🔑 Key Takeaways1. AI Won't Fully Automate Coding Jobs
* More like enhanced assistants than replacements
* Human oversight remains critical
* Trust and context are irreplaceable
-
Efficiency Gains Are Limited
- Real-world ceiling around 2x improvement
- Not the exponential gains often promised
- Similar to other parallelization efforts
-
Success Factors for Agentic Coding
-
Well-organized human-in-the-loop processes
- Clear review and approval workflows
- Incremental trust building
- Realistic expectations
🔬 Research References* Princeton AI research on agent limitations * "AI Agents That Matter" paper findings * Empirical evidence of diminishing returns * Real-world case studies
💡 Practical ImplicationsFor Developers:* Focus on optimizing the human review process * Build better UI/UX for code review * Implement incremental deployment strategies
For Organizations:* Set realistic productivity expectations * Invest in human-agent collaboration tools * Don't expect 10x improvements from more agents
For the Industry:* Paradigm shift from "replacement" to "augmentation" * Need for new metrics beyond raw speed * Focus on quality over quantity of agents
🎬 Episode Structure1. Hook: The soup cooking analogy 2. Theory: Amdahl's Law explanation 3. Traditional: Computing bottlenecks 4. Modern: Agentic coding bottlenecks 5. Reality Check: The 2x ceiling 6. Future: Optimizing within constraints
🗣️ Quotable Moments* "10 agents don't code 10 times faster, just like 10 cooks don't make soup 10 times faster" * "Humans are the new I/O bottleneck" * "You can't parallelize trust" * "The theoretical max is 2x faster - that's the reality check"
🤔 Discussion Questions1. Is the 2x ceiling permanent or can we innovate around it? 2. What's more valuable: speed or code quality? 3. How do we optimize the human bottleneck? 4. Will future AI models change these limitations?
📝 Episode Tagline"When infinite AI agents hit the wall of human review, Amdahl's Law reminds us that some things just can't be parallelized - including trust, context, and the courage to deploy to production."
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The plastic shamans of OpenAI 🔥 Hot Course Offers:- 🤖 Master GenAI Engineering - Build Production AI Systems- 🦀 Learn Professional Rust - Industry-Grade Development- 📊 AWS AI & Analytics - Scale Your ML in Cloud- ⚡ Production GenAI on AWS - Deploy at Enterprise Scale- 🛠️ Rust DevOps Mastery - Automate Everything🚀 Level Up Your Career:- 💼 Production ML Program - Complete MLOps & Cloud Mastery- 🎯 Start Learning Now - Fast-Track Your ML Career- 🏢 Trusted by Fortune 500 TeamsLearn end-to-end ML engineering from industry veterans at PAIML.COM
Dangerous Dilettantes vs. Toyota Way EngineeringCore ThesisThe influx of AI-powered automation tools creates dangerous dilettantes - practitioners who know just enough to be harmful. The Toyota Production System (TPS) principles provide a battle-tested framework for integrating automation while maintaining engineering discipline.
Historical Context
Toyota Way formalized ~2001DevOps principles derive from TPSCoincided with post-dotcom crash startupsDecades of manufacturing automation parallels modern AI-based automation
Dangerous Dilettante Indicators* Promises magical automation without understanding systems
* Focuses on short-term productivity gains over long-term stability
* Creates interfaces that hide defects rather than surfacing them
* Lacks understanding of production engineering fundamentals
* Prioritizes feature velocity over deterministic behavior
Toyota Way Implementation for AI-Enhanced Development1. Long-Term Philosophy Over Short-Term Gains
// Anti-pattern: Brittle automation scriptlet quick_fix = agent.generate_solution(problem, { optimize_for: "immediate_completion", validation: false});// TPS approach: Sustainable system designlet sustainable_solution = engineering_system .with_agent_augmentation(agent) .design_solution(problem, { time_horizon_years: 2, observability: true, test_coverage_threshold: 0.85, validate_against_principles: true });
* Build systems that remain maintainable across years
* Establish deterministic validation criteria before implementation
* Optimize for total cost of ownership, not just initial development
- Create Continuous Process Flow to Surface Problems* Implement CI pipelines that surface defects immediately:
- Static analysis validation
- Type checking (prefer strong type systems)
- Property-based testing
- Integration tests
- Performance regression detection
Build flow:make lint → make typecheck → make test → make integration → make benchmarkFail fast at each stage
* Force errors to surface early rather than be hidden by automation
* Agent-assisted development must enhance visibility, not obscure it
- Pull Systems to Prevent Overproduction* Minimize code surface area - only implement what's needed
- Prefer refactoring to adding new abstractions
- Use agents to eliminate boilerplate, not to generate speculative features
// Prefer minimal implementationsfunction processData(data: T[]): Result { // Use an agent to generate only the exact transformation needed // Not to create a general-purpose framework}
4. Level Workload (Heijunka)* Establish consistent development velocity
* Avoid burst patterns that hide technical debt
* Use agents consistently for small tasks rather than large sporadic generations
- Build Quality In (Jidoka)
Automate failure detection, not just productionAny failed test/lint/check = full system halt - Every team member empowered to "pull the andon cord" (stop integration)
- AI-assisted code must pass same quality gates as human code
-
Quality gates should be more rigorous with automation, not less
-
Standardized Tasks and Processes* Uniform build system interfaces across projects
- Consistent command patterns:
make formatmake lintmake testmake deploy - Standardized ways to integrate AI assistance
-
Documented patterns for human verification of generated code
-
Visual Controls to Expose Problems* Dashboards for code coverage
- Complexity metrics
- Dependency tracking
- Performance telemetry
-
Use agents to improve these visualizations, not bypass them
-
Reliable, Thoroughly-Tested Technology* Prefer languages with strong safety guarantees (Rust, OCaml, TypeScript over JS)
- Use static analysis tools (clippy, eslint)
- Property-based testing over example-based
```
[test]fn property_based_validation() { proptest!(|(input: Vec)| { let result = process(&input); // Must hold for all inputs assert!(result.is_valid_state()); });}
``` 9. Grow Leaders Who Understand the Work* Engineers must understand what agents produce * No black-box implementations * Leaders establish a culture of comprehension, not just completion
- Develop Exceptional Teams* Use AI to amplify team capabilities, not replace expertise
- Agents as team members with defined responsibilities
-
Cross-training to understand all parts of the system
-
Respect Extended Network (Suppliers)* Consistent interfaces between systems
- Well-documented APIs
- Version guarantees
-
Explicit dependencies
-
Go and See (Genchi Genbutsu)* Debug the actual system, not the abstraction
- Trace problematic code paths
- Verify agent-generated code in context
- Set up comprehensive observability
// Instrument code to make the invisible visiblefunc ProcessRequest(ctx context.Context, req *Request) (*Response, error) { start := time.Now() defer metrics.RecordLatency("request_processing", time.Since(start)) // Log entry point logger.WithField("request_id", req.ID).Info("Starting request processing") // Processing with tracing points // ... // Verify exit conditions if err != nil { metrics.IncrementCounter("processing_errors", 1) logger.WithError(err).Error("Request processing failed") } return resp, err}
13. Make Decisions Slowly by Consensus* Multi-stage validation for significant architectural changes
* Automated analysis paired with human review
* Design documents that trace requirements to implementation
- Kaizen (Continuous Improvement)* Automate common patterns that emerge
- Regular retrospectives on agent usage
- Continuous refinement of prompts and integration patterns
Technical Implementation PatternsAI Agent Integration
interface AgentIntegration { // Bounded scope generateComponent(spec: ComponentSpec): Promise<{ code: string; testCases: TestCase[]; knownLimitations: string[]; }>; // Surface problems validateGeneration(code: string): Promise; // Continuous improvement registerFeedback(generation: string, feedback: Feedback): void;}
Safety Control Systems* Rate limiting
* Progressive exposure
* Safety boundaries
* Fallback mechanisms
* Manual oversight thresholds
Example: CI Pipeline with Agent Integration ```
ci-pipeline.ymlstages: - lint - test - integrate - deploylint: script: - make format-check - make lint # Agent-assisted code must pass same checks - make ai-validation test: script: - make unit-test - make property-test - make coverage-report # Coverage thresholds enforced - make coverage-validation# ...
``` ConclusionAgents provide useful automation when bounded by rigorous engineering practices. The Toyota Way principles offer proven methodology for integrating automation without sacrificing quality. The difference between a dangerous dilettante and an engineer isn't knowledge of the latest tools, but understanding of fundamental principles that ensure reliable, maintainable systems.
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Extensive Notes: The Truth About AI and Your Coding JobTypes of AI Narrow AI*
+ Not truly intelligent
+ Pattern matching and full text search
+ Examples: voice assistants, coding autocomplete
+ Useful but contains bugs
+ Multiple narrow AI solutions compound bugs
+ Get in, use it, get out quickly
-
AGI (Artificial General Intelligence)
- No evidence we're close to achieving this
- May not even be possible
- Would require human-level intelligence
- Needs consciousness to exist
- Consciousness: ability to recognize what's happening in environment
- No concept of this in narrow AI approaches
- Pure fantasy and magical thinking
-
ASI (Artificial Super Intelligence)
-
Even more fantasy than AGI
- No evidence at all it's possible
- More science fiction than reality
The DevOps Flowchart Test1. Can you explain what DevOps is?
* If no → You're incompetent on this topic
* If yes → Continue to next question
-
Does your company use DevOps?
- If no → You're inexperienced and a magical thinker
- If yes → Continue to next question
-
Why would you think narrow AI has any form of intelligence?
-
Anyone claiming AI will automate coding jobs while understanding DevOps is likely:
- A magical thinker
- Unaware of scientific process
- A grifter
Why DevOps Matters* Proven methodology similar to Toyota Way * Based on continuous improvement (Kaizen) * Look-and-see approach to reducing defects * Constantly improving build systems, testing, linting * No AI component other than basic statistical analysis * Feedback loop that makes systems better
The Reality of Job Automation* People who do nothing might be eliminated + Not AI automating a job if they did nothing * Workers who create negative value + People who create bugs at 2AM + Their elimination isn't AI automation
Measuring Software Quality* High churn files correlate with defects * Constant changes to same file indicate not knowing what you're doing * DevOps patterns help identify issues through: + Tracking file changes + Measuring complexity + Code coverage metrics + Deployment frequency
Conclusion* Very early stages of combining narrow AI with DevOps * Narrow AI tools are useful but limited * Need to look beyond magical thinking * Opinions don't matter if you: + Don't understand DevOps + Don't use DevOps + Claim to understand DevOps but believe narrow AI will replace developers
Raw Assessment* If you don't understand DevOps → Your opinion doesn't matter * If you understand DevOps but don't use it → Your opinion doesn't matter * If you understand and use DevOps but think AI will automate coding jobs → You're likely a magical thinker or grifter
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Extensive Notes: "No Dummy: AI Will Not Replace Coders"Introduction: The Critical Thinking Problem* America faces a critical thinking deficit, especially evident in narratives about AI automating developers' jobs * Speaker advocates for examining the narrative with core critical thinking skills * Suggests substituting the dominant narrative with alternative explanations
Alternative Explanation 1: Non-Productive Employees* Organizations contain people who do "absolutely nothing" * If you fire a person who does no work, there will be no impact * These non-productive roles exist in academics, management, and technical industries * Reference to David Graeber's book "Bullshit Jobs" which categorizes meaningless jobs: + Task masters + Box tickers + Goons * When these jobs are eliminated, AI didn't replace them because "the job didn't need to exist"
Alternative Explanation 2: Low-Skilled Developers* Some developers have "very low or no skills, even negative skills" * Firing someone who writes "buggy code" and replacing them with a more productive developer (even one using auto-completion tools) isn't AI replacing a job * These developers have "negative value to an organization" * Removing such developers would improve the company regardless of automation * Using better tools, CI/CD, or software engineering best practices to compensate for their removal isn't AI replacement
Alternative Explanation 3: Basic Automation with Traditional Tools* Software engineers have been automating tasks for decades without AI * Speaker's example: At Disney Future Animation (2003), replaced manual weekend maintenance with bash scripts * "A bash script is not AI. It has no form of intelligence. It's a for loop with some conditions in it." * Many companies have poor processes that can be easily automated with basic scripts * This automation has "absolutely nothing to do with AI" and has "been happening for the history of software engineering"
Alternative Explanation 4: Narrow vs. General Intelligence* Useful applications of machine learning exist: + Linear regression + K-means clustering + Autocompletion + Transcription * These are "narrow components" with "zero intelligence" * Each component does a specific task, not general intelligence * "When someone says you automated a job with a large language model, what are you talking about? It doesn't make sense." * LLMs are not intelligent; they're task-based systems
Alternative Explanation 5: Outsourcing* Companies commonly outsource jobs to lower-cost regions * Jobs claimed to be "taken by AI" may have been outsourced to India, Mexico, or China * This practice is common in America despite questionable ethics * Organizations may falsely claim AI automation when they've simply outsourced work
Alternative Explanation 6: Routine Corporate Layoffs* Large companies routinely fire ~3% of their workforce (Apple, Amazon mentioned) * Fear is used as a motivational tool in "toxic American corporations" * The "AI is coming for your job" narrative creates fear and motivation * More likely explanations: non-productive employees, low-skilled workers, simple automation, etc.
The Marketing and Sales Deception* CEOs (specifically mentions Anthropic and OpenAI) make false claims about agent capabilities * "The CEO of a company like Anthropic... is a liar who said that software engineering jobs will be automated with agents" * Speaker claims to have used these tools and found "they have no concept of intelligence" * Sam Altman (OpenAI) characterized as "a known liar" who "exaggerates about everything" * Marketing people with no software engineering background make claims about coding automation * Companies like NVIDIA promote AI hype to sell GPUs
Conclusion: The Real Problem* "AI" is a misnomer for large language models * These are "narrow intelligence" or "narrow machine learning" systems * They "do one task like autocomplete" and chain these tasks together * There is "no concept of intelligence embedded inside" * The speaker sees a bigger issue: lack of critical thinking in America * Warns that LLMs are "dumb as a bag of rocks" but powerful tools * Left in inexperienced hands, these tools could create "catastrophic software" * Rejects the narrative that "AI will replace software engineers" as having "absolutely zero evidence"
Key Quotes
"We have a real problem with critical thinking in America. And one of the places that is very evident is this false narrative that's been spread about AI automating developers jobs."
"If you fire a person that does no work, there will be no impact."
"I have been automating people's jobs my entire life... That's what I've been doing with basic scripts. A bash script is not AI."
"Large language models are not intelligent. How could they possibly be this mystical thing that's automating things?"
"By saying that AI is going to come for your job soon, it's a great false narrative to spread fear where people worry about all the AI is coming."
"Much more likely the story of AI is that it is a very powerful tool that is dumb as a bag of rocks and left into the hands of the inexperienced and the naive and the fools could create catastrophic software that we don't yet know how bad the effects will be."
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how Gen.AI companies combine narrow ML components behind conversational interfaces to simulate intelligence. Each agent component (text generation, context management, tool integration) has direct non-ML equivalents. API access bypasses the deceptive UI layer, providing better determinism and utility. Optimal usage requires abandoning open-ended interactions for narrow, targeted prompting focused on pattern recognition tasks where these systems actually deliver value.
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Episode Summary:A critical examination of generative AI through the lens of a null hypothesis, comparing it to a sophisticated search engine over all intellectual property ever created, challenging our assumptions about its transformative nature.
Keywords:AI demystification, null hypothesis, intellectual property, search engines, large language models, code generation, machine learning operations, technical debt, AI ethics
Why This Matters to Your Organization:Understanding AI's true capabilities—beyond the hype—is crucial for making strategic technology decisions. Is your team building solutions based on AI's actual strengths or its perceived magic?
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AIReality #TechDemystified #DataScience #PragmaticAI #NullHypothesis
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Episode Notes: Claude Code Review: Pattern Matching, Not IntelligenceSummaryI share my hands-on experience with Anthropic's Claude Code tool, praising its utility while challenging the misleading "AI" framing. I argue these are powerful pattern matching tools, not intelligent systems, and explain how experienced developers can leverage them effectively while avoiding common pitfalls.
Key Points* Claude Code offers genuine productivity benefits as a terminal-based coding assistant * The tool excels at make files, test creation, and documentation by leveraging context * "AI" is a misleading term - these are pattern matching and data mining systems * Anthropomorphic interfaces create dangerous illusions of competence * Most valuable for experienced developers who can validate suggestions * Similar to combining CI/CD systems with data mining capabilities, plus NLP * The user, not the tool, provides the critical thinking and expertise
Quote"The intelligence is coming from the human. It's almost like a combination of pattern matching tools combined with traditional CI/CD tools."
Best Use Cases* Test-driven development * Refactoring legacy code * Converting between languages (JavaScript → TypeScript) * Documentation improvements * API work and Git operations * Debugging common issues
Risky Use Cases* Legacy systems without sufficient training patterns * Cutting-edge frameworks not in training data * Complex architectural decisions requiring system-wide consistency * Production systems where mistakes could be catastrophic * Beginners who can't identify problematic suggestions
Next Steps* Frame these tools as productivity enhancers, not "intelligent" agents * Use alongside existing development tools like IDEs * Maintain vigilant oversight - "watch it like a hawk" * Evaluate productivity gains realistically for your specific use cases
ClaudeCode #DeveloperTools #PatternMatching #AIReality #ProductivityTools #CodingAssistant #TerminalTools
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Deno: The Modern TypeScript Runtime Alternative to PythonEpisode SummaryDeno stands tall. TypeScript runs fast in this Rust-based runtime. It builds standalone executables and offers type safety without the headaches of Python's packaging and performance problems.
KeywordsDeno, TypeScript, JavaScript, Python alternative, V8 engine, scripting language, zero dependencies, security model, standalone executables, Rust complement, DevOps tooling, microservices, CLI applications
Key Benefits Over Python Built-in TypeScript Support*
+ First-class TypeScript integration
+ Static type checking improves code quality
+ Better IDE support with autocomplete and error detection
+ Types catch errors before runtime
-
Superior Performance
- V8 engine provides JIT compilation optimizations
- Significantly faster than CPython for most workloads
- No Global Interpreter Lock (GIL) limiting parallelism
- Asynchronous operations are first-class citizens
- Better memory management with V8's garbage collector
-
Zero Dependencies Philosophy
-
No package.json or external package manager
- URLs as imports simplify dependency management
- Built-in standard library for common operations
- No node_modules folder
- Simplified dependency auditing
-
Modern Security Model
-
Explicit permissions for file, network, and environment access
- Secure by default - no arbitrary code execution
- Sandboxed execution environment
-
Simplified Bundling and Distribution
-
Compile to standalone executables
- Consistent execution across platforms
- No need for virtual environments
- Simplified deployment to production
Real-World Usage Scenarios* DevOps tooling and automation * Microservices and API development * Data processing applications * CLI applications with standalone executables * Web development with full-stack TypeScript * Enterprise applications with type-safe business logic
Complementing Rust* Perfect scripting companion to Rust's philosophy * Shared focus on safety and developer experience * Unified development experience across languages * Possibility to start with Deno and migrate performance-critical parts to Rust
Coming in May: New courses on Deno from Pragmatic A-Lapse
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Episode Notes: The Wizard of AI: Unmasking the Smoke and MirrorsSummaryI expose the reality behind today's "AI" hype. What we call AI is actually generative search and pattern matching - useful but not intelligent. Like the Wizard of Oz, tech companies use smoke and mirrors to market what are essentially statistical models as sentient beings.
Key Points* Current AI technologies are statistical pattern matching systems, not true intelligence * The term "artificial intelligence" is misleading - these are advanced search tools without consciousness * We should reframe generative AI as "generative search" or "generative pattern matching" * AI systems hallucinate, recommend non-existent libraries, and create security vulnerabilities * Similar technology hype cycles (dot-com, blockchain, big data) all followed the same pattern * Successful implementation requires treating these as IT tools, not magical solutions * Companies using misleading AI terminology (like "cognitive" and "intelligence") create unrealistic expectations
Quote"At the heart of intelligence is consciousness... These statistical pattern matching systems are not aware of the situation they're in."
Resources* Framework: Apply DevOps and Toyota Way principles when implementing AI tools * Historical Example: Amazon "walkout technology" that actually relied on thousands of workers in India
Next Steps* Remove "AI" terminology from your organization's solutions * Build on existing quality control frameworks (deterministic techniques, human-in-the-loop) * Outcompete competitors by understanding the real limitations of these tools
AIReality #GenerativeSearch #PatternMatching #TechHype #AIImplementation #DevOps #CriticalThinking
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Episode Notes: Search, Not Superintelligence: RAG's Role in Grounding Generative AISummaryI demystify RAG technology and challenge the AI hype cycle. I argue current AI is merely advanced search, not true intelligence, and explain how RAG grounds models in verified data to reduce hallucinations while highlighting its practical implementation challenges.
Key Points* Generative AI is better described as "generative search" - pattern matching and prediction, not true intelligence * RAG (Retrieval-Augmented Generation) grounds AI by constraining it to search within specific vector databases * Vector databases function like collaborative filtering algorithms, finding similarity in multidimensional space * RAG reduces hallucinations but requires extensive data curation - a significant challenge for implementation * AWS Bedrock provides unified API access to multiple AI models and knowledge base solutions * Quality control principles from Toyota Way and DevOps apply to AI implementation * "Agents" are essentially scripts with constraints, not truly intelligent entities
Quote"We don't have any form of intelligence, we just have a brute force tool that's not smart at all, but that is also very useful."
Resources* AWS Bedrock: https://aws.amazon.com/bedrock/ * Vector Database Overview: https://ds500.paiml.com/subscribe.html
Next Steps* Next week: Coding implementation of RAG technology * Explore AWS knowledge base setup options * Consider data curation requirements for your organization
GenerativeAI #RAG #VectorDatabases #AIReality #CloudComputing #AWS #Bedrock #DataScience
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Pragmatica Labs Podcast: Interactive Labs UpdateEpisode NotesAnnouncement: Updated Interactive Labs* New version of interactive labs now available on the Pragmatica Labs platform * Focus on improved Rust teaching capabilities
Rust Learning Environment Features* Browser-based development environment with: + Ability to create projects with Cargo + Code compilation functionality + Visual Studio Code in the browser * Access to source code from dozens of Rust courses
Pragmatica Labs Rust Course Offerings* Applied Rust courses covering: + GUI development + Serverless + Data engineering + AI engineering + MLOps + Community tools + Python and Rust integration
Upcoming Technology Coverage* Local large language models (Olamma) * Zig as a modern C replacement * WebSockets + Building custom terminals + Interactive data engineering dashboards with SQLite integration * WebAssembly + Assembly-speed performance in browsers
Conclusion* New content and courses added weekly * Interactive labs now live on the platform * Visit PAIML.com to explore and provide feedback
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Meta and OpenAI Book Piracy Controversy: Podcast SummaryThe Unauthorized Data Acquisition* Meta (Facebook's parent company) and OpenAI downloaded millions of pirated books from Library Genesis (LibGen) to train artificial intelligence models * The pirated collection contained approximately 7.5 million books and 81 million research papers * Mark Zuckerberg reportedly authorized the use of this unauthorized material * The podcast host discovered all ten of his published books were included in the pirated database
Deliberate Policy Violations* Internal communications reveal Meta employees recognized legal risks * Staff implemented measures to conceal their activities: + Removing copyright notices + Deleting ISBN numbers + Discussing "medium-high legal risk" while proceeding * Organizational structure resembled criminal enterprises: leadership approval, evidence concealment, risk calculation, delegation of questionable tasks
Legal Challenges* Authors including Sarah Silverman have filed copyright infringement lawsuits * Both companies claim protection under "fair use" doctrine * BitTorrent download method potentially involved redistribution of pirated materials * Courts have not yet ruled on the legality of training AI with copyrighted material
Ethical Considerations* Contradiction between public statements about "responsible AI" and actual practices * Attribution removal prevents proper credit to original creators * No compensation provided to authors whose work was appropriated * Employee discomfort evident in statements like "torrenting from a corporate laptop doesn't feel right"
Broader Implications* Represents a form of digital colonization * Transforms intellectual resources into corporate assets without permission * Exploits creative labor without compensation * Undermines original purpose of LibGen (academic accessibility) for corporate profit
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Rust Multiple Entry Points: Architectural PatternsKey Points Core Concept: Multiple entry points in Rust enable single codebase deployment across CLI, microservices, WebAssembly and GUI contexts
* Implementation Path: Initial CLI development → Web API → Lambda/cloud functions
* Cargo Integration*: Native support via src/bin directory or explicit binary targets in Cargo.toml
Technical Advantages Memory Safety: Consistent safety guarantees across deployment targets * Type Consistency: Strong typing ensures API contract integrity between interfaces * Async Model: Unified asynchronous execution model across environments * Binary Optimization: Compile-time optimizations yield superior performance vs runtime interpretation * Ownership Model*: No-saved-state philosophy aligns with Lambda execution context
Deployment Architecture Core Logic Isolation: Business logic encapsulated in library crates * Interface Separation: Entry point-specific code segregated from core functionality * Build Pipeline: Single compilation source enables consistent artifact generation * Infrastructure Consistency: Uniform deployment targets eliminate environment-specific bugs * Resource Optimization*: Shared components reduce binary size and memory footprint
Implementation Benefits Iteration Speed: CLI provides immediate feedback loop during core development * Security Posture: Memory safety extends across all deployment targets * API Consistency: JSON payload structures remain identical between CLI and web interfaces * Event Architecture: Natural alignment with event-driven cloud function patterns * Compile-Time Optimizations*: CPU-specific enhancements available at binary generation
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Podcast Notes: Vibe Coding & The Maintenance Problem in Software EngineeringEpisode SummaryIn this episode, I explore the concept of "vibe coding" - using large language models for rapid software development - and compare it to Python's historical role as "vibe coding 1.0." I discuss why focusing solely on development speed misses the more important challenge of maintaining systems over time.
Key PointsWhat is Vibe Coding?* Using large language models to do the majority of development * Getting something working quickly and putting it into production * Similar to prototyping strategies used for decades
Python as "Vibe Coding 1.0"* Python emerged as a reaction to complex languages like C and Java * Made development more readable and accessible * Prioritized developer productivity over CPU time * Initially sacrificed safety features like static typing and true threading (though has since added some)
The Real Problem: System Maintenance, Not Development Speed* Production systems need continuous improvement, not just initial creation * Software is organic (like a fig tree) not static (like a playground) * Need to maintain, nurture, and respond to changing conditions * "The problem isn't, and it's never been, about how quick you can create software"
The Fig Tree vs. Playground Analogy Playground/House/Bridge: Build once, minimal maintenance, fixed design * Fig Tree*: Requires constant attention, responds to environment, needs protection from pests, requires pruning and care * Software is much more like the fig tree - organic and needing continuous maintenance
Dangers of Prioritizing Development Speed* Python allowed freedom but created maintenance challenges: + No compiler to catch errors before deployment + Lack of types leading to runtime errors + Dead code issues + Mutable variables by default * "Every time you write new Python code, you're creating a problem"
Recommendations for Using AI Tools* Focus on building systems you can maintain for 10+ years * Consider languages like Rust with strong safety features * Use AI tools to help with boilerplate and API exploration * Ensure code is understood by the entire team * Get advice from practitioners who maintain large-scale systems
Final ThoughtsPython itself is a form of vibe coding - it pushes technical complexity down the road, potentially creating existential threats for companies with poor maintenance practices. Use new tools, but maintain the mindset that your goal is to build maintainable systems, not just generate code quickly.
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Podcast Notes: DeepSeek R2 - The Tech Stock "Atom Bomb"Overview* DeepSeek R2 could heavily impact tech stocks when released (April or May 2025) * Could threaten OpenAI, Anthropic, and major tech companies * US tech market already showing weakness (Tesla down 50%, NVIDIA declining)
Cost Claims DeepSeek R2 claims to be 40 times cheaper* than competitors * Suggests AI may not be as profitable as initially thought * Could trigger a "race to zero" in AI pricing
NVIDIA Concerns* NVIDIA's high stock price depends on GPU shortage continuing * If DeepSeek can use cheaper, older chips efficiently, threatens NVIDIA's model * Ironically, US chip bans may have forced Chinese companies to innovate more efficiently
The Cloud Computing Comparison* AI could follow cloud computing's path (AWS → Azure → Google → Oracle) * Becoming a commodity with shrinking profit margins * Basic AI services could keep getting cheaper ($20/month now, likely lower soon)
Open Source Advantage* Like Linux vs Windows, open source AI could dominate * Most databases and programming languages are now open source * Closed systems may restrict innovation
Global AI Landscape* Growing distrust of US tech companies globally * Concerns about data privacy and government surveillance * Countries might develop their own AI ecosystems * EU could lead in privacy-focused AI regulation
AI Reality Check* LLMs are "sophisticated pattern matching," not true intelligence * Compare to self-checkout: automation helps but humans still needed * AI will be a tool that changes work, not a replacement for humans
Investment Impact* Tech stocks could lose significant value in next 2-6 months * Chip makers might see reduced demand * Investment could shift from AI hardware to integration companies or other sectors
Conclusion* DeepSeek R2 could trigger "cascading failure" in big tech * More focus on local, decentralized AI solutions * Human-in-the-loop approach likely to prevail * Global tech landscape could look very different in 10 years
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Regulatory Capture in Artificial Intelligence Markets: Oligopolistic Preservation StrategiesThesis StatementAnalysis of emergent regulatory capture mechanisms employed by dominant AI firms (OpenAI, Anthropic) to establish market protectionism through national security narratives.
Historiographical Parallels: Microsoft Anti-FOSS Campaign (1990s) Halloween Documents: Systematic FUD dissemination characterizing Linux as ideological threat ("communism") * Outcome Falsification: Contradictory empirical results with >90% infrastructure adoption of Linux in contemporary computing environments * Innovation Suppression Effects*: Demonstrated retardation of technological advancement through monopolistic preservation strategies
Tactical Analysis: OpenAI Regulatory ManeuversGeopolitical Framing Attribution Fallacy: Unsubstantiated classification of DeepSeek as state-controlled entity * Contradictory Empirical Evidence: Public disclosure of methodologies, parameter weights indicating superior transparency compared to closed-source implementations * Policy Intervention Solicitation*: Executive advocacy for governmental prohibition of PRC-developed models in allied jurisdictions
Technical Argumentation Deficiencies Logical Inconsistency: Assertion of security vulnerabilities despite absence of data collection mechanisms in open-weight models * Methodological Contradiction: Accusation of knowledge extraction despite parallel litigation against OpenAI for copyrighted material appropriation * Security Paradox*: Open-weight systems demonstrably less susceptible to covert vulnerabilities through distributed verification mechanisms
Tactical Analysis: Anthropic Regulatory ManeuversValue Preservation Rhetoric IP Valuation Claim: Assertion of "$100 million secrets" in minimal codebases * Contradictory Value Proposition: Implicit acknowledgment of artificial valuation differentials between proprietary and open implementations * Predictive Overreach*: Statistically improbable claims regarding near-term code generation market capture (90% in 6 months, 100% in 12 months)
National Security Integration Espionage Allegation: Unsubstantiated claims of industrial intelligence operations against AI firms * Intelligence Community Alignment: Explicit advocacy for intelligence agency protection of dominant market entities * Export Control Amplification*: Lobbying for semiconductor distribution restrictions to constrain competitive capabilities
Economic Analysis: Underlying Motivational StructuresPerfect Competition Avoidance Profit Nullification Anticipation: Recognition of zero-profit equilibrium in commoditized markets * Artificial Scarcity Engineering: Regulatory frameworks as mechanism for maintaining supra-competitive pricing structures * Valuation Preservation Imperative*: Existential threat to organizations operating with negative profit margins and speculative valuations
Regulatory Capture Mechanisms Resource Diversion: Allocation of public resources to preserve private rent-seeking behavior * Asymmetric Regulatory Impact: Disproportionate compliance burden on small-scale and open-source implementations * Innovation Concentration Risk*: Technological advancement limitations through artificial competition constraints
Conclusion: Policy ImplicationsRegulatory frameworks ostensibly designed for security enhancement primarily function as competition suppression mechanisms, with demonstrable parallels to historical monopolistic preservation strategies. The commoditization of AI capabilities represents the fundamental threat to current market leaders, with national security narratives serving as instrumental justification for market distortion.
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The Rust Paradox: Systems Programming in the Epoch of Generative AII. Paradoxical Thesis Examination Contradictory Technological Narratives*
+ Epistemological inconsistency: programming simultaneously characterized as "automatable" yet Rust deemed "excessively complex for acquisition"
+ Logical impossibility of concurrent validity of both propositions establishes fundamental contradiction
+ Necessitates resolution through bifurcation theory of programming paradigms
-
Rust Language Adoption Metrics (2024-2025)
- Subreddit community expansion: +60,000 users (2024)
- Enterprise implementation across technological oligopoly: Microsoft, AWS, Google, Cloudflare, Canonical
- Linux kernel integration represents significant architectural paradigm shift from C-exclusive development model
II. Performance-Safety Dialectic in Contemporary Engineering Empirical Performance Coefficients*
+ Ruff Python linter: 10-100× performance amplification relative to predecessors
+ UV package management system demonstrating exponential efficiency gains over Conda/venv architectures
+ Polars exhibiting substantial computational advantage versus pandas in data analytical workflows
-
Memory Management Architecture
- Ownership-based model facilitates deterministic resource deallocation without garbage collection overhead
- Performance characteristics approximate C/C++ while eliminating entire categories of memory vulnerabilities
- Compile-time verification supplants runtime detection mechanisms for concurrency hazards
III. Programmatic Bifurcation Hypothesis Dichotomous Evolution Trajectory*
+ Application layer development: increasing AI augmentation, particularly for boilerplate/templated implementations
+ Systems layer engineering: persistent human expertise requirements due to precision/safety constraints
+ Pattern-matching limitations of generative systems insufficient for systems-level optimization requirements
-
Cognitive Investment Calculus
- Initial acquisition barrier offset by significant debugging time reduction
- Corporate training investment persisting despite generative AI proliferation
- Market valuation of Rust expertise increasing proportionally with automation of lower-complexity domains
IV. Neuromorphic Architecture Constraints in Code Generation LLM Fundamental Limitations*
+ Pattern-recognition capabilities distinct from genuine intelligence
+ Analogous to mistaking k-means clustering for financial advisory services
+ Hallucination phenomena incompatible with systems-level precision requirements
-
Human-Machine Complementarity Framework
- AI functioning as expert-oriented tool rather than autonomous replacement
- Comparable to CAD systems requiring expert oversight despite automation capabilities
- Human verification remains essential for safety-critical implementations
V. Future Convergence Vectors Synergistic Integration Pathways*
+ AI assistance potentially reducing Rust learning curve steepness
+ Rust's compile-time guarantees providing essential guardrails for AI-generated implementations
+ Optimal professional development trajectory incorporating both systems expertise and AI utilization proficiency
-
Economic Implications
- Value migration from general-purpose to systems development domains
- Increasing premium on capabilities resistant to pattern-based automation
- Natural evolutionary trajectory rather than paradoxical contradiction
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Podcast Notes: Debunking Claims About AI's Future in CodingEpisode Overview* Analysis of Anthropic CEO Dario Amodei's claim: "We're 3-6 months from AI writing 90% of code, and 12 months from AI writing essentially all code" * Systematic examination of fundamental misconceptions in this prediction * Technical analysis of GenAI capabilities, limitations, and economic forces
- Terminological Misdirection Category Error*: Using "AI writes code" fundamentally conflates autonomous creation with tool-assisted composition
- Tool-User Relationship: GenAI functions as sophisticated autocomplete within human-directed creative process
- Equivalent to claiming "Microsoft Word writes novels" or "k-means clustering automates financial advising"
- Orchestration Reality: Humans remain central to orchestrating solution architecture, determining requirements, evaluating output, and integration
-
Cognitive Architecture: LLMs are prediction engines lacking intentionality, planning capabilities, or causal understanding required for true "writing"
-
AI Coding = Pattern Matching in Vector Space Fundamental Limitation*: LLMs perform sophisticated pattern matching, not semantic reasoning
- Verification Gap: Cannot independently verify correctness of generated code; approximates solutions based on statistical patterns
- Hallucination Issues: Tools like GitHub Copilot regularly fabricate non-existent APIs, libraries, and function signatures
- Consistency Boundaries: Performance degrades with codebase size and complexity; particularly with cross-module dependencies
-
Novel Problem Failure: Performance collapses when confronting problems without precedent in training data
-
The Last Mile Problem Integration Challenges*: Significant manual intervention required for AI-generated code in production environments
- Security Vulnerabilities: Generated code often introduces more security issues than human-written code
- Requirements Translation: AI cannot transform ambiguous business requirements into precise specifications
- Testing Inadequacy: Lacks context/experience to create comprehensive testing for edge cases
-
Infrastructure Context: No understanding of deployment environments, CI/CD pipelines, or infrastructure constraints
-
Economics and Competition Realities Open Source Trajectory*: Critical infrastructure historically becomes commoditized (Linux, Python, PostgreSQL, Git)
- Zero Marginal Cost: Economics of AI-generated code approaching zero, eliminating sustainable competitive advantage
- Negative Unit Economics: Commercial LLM providers operate at loss per query for complex coding tasks
- Inference costs for high-token generations exceed subscription pricing
- Human Value Shift: Value concentrating in requirements gathering, system architecture, and domain expertise
-
Rising Open Competition: Open models (Llama, Mistral, Code Llama) rapidly approaching closed-source performance at fraction of cost
-
False Analogy: Tools vs. Replacements Tool Evolution Pattern*: GenAI follows historical pattern of productivity enhancements (IDEs, version control, CI/CD)
- Productivity Amplification: Enhances developer capabilities rather than replacing them
- Cognitive Offloading: Handles routine implementation tasks, enabling focus on higher-level concerns
- Decision Boundaries: Majority of critical software engineering decisions remain outside GenAI capabilities
- Historical Precedent: Despite 50+ years of automation predictions, development tools consistently augment rather than replace developers
Key Takeaway* GenAI coding tools represent significant productivity enhancement but fundamental mischaracterization to frame as "AI writing code" * More likely: GenAI companies face commoditization pressure from open-source alternatives than developers face replacement
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Pattern Matching vs. Content Comprehension: The Mathematical Case Against "Reading = Training"Mathematical Foundations of the Distinction Dimensional processing divergence*
+ Human reading: Sequential, unidirectional information processing with neural feedback mechanisms
+ ML training: Multi-dimensional vector space operations measuring statistical co-occurrence patterns
+ Core mathematical operation: Distance calculations between points in n-dimensional space
-
Quantitative threshold requirements
- Pattern matching statistical significance: n >> 10,000 examples
- Human comprehension threshold: n < 100 examples
- Logarithmic scaling of effectiveness with dataset size
-
Information extraction methodology
-
Reading: Temporal, context-dependent semantic comprehension with structural understanding
- Training: Extraction of probability distributions and distance metrics across the entire corpus
- Different mathematical operations performed on identical content
The Insufficiency of Limited Datasets Centroid instability principle*
+ K-means clustering with insufficient data points creates mathematically unstable centroids
+ High variance in low-data environments yields unreliable similarity metrics
+ Error propagation increases exponentially with dataset size reduction
-
Annotation density requirement
- Meaningful label extraction requires contextual reinforcement across thousands of similar examples
- Pattern recognition systems produce statistically insignificant results with limited samples
- Mathematical proof: Signal-to-noise ratio becomes unviable below certain dataset thresholds
Proprietorship and Mathematical Information Theory Proprietary information exclusivity*
+ Coca-Cola formula analogy: Constrained mathematical solution space with intentionally limited distribution
+ Sales figures for tech companies (Tesla/NVIDIA): Isolated data points without surrounding distribution context
+ Complete feature space requirement: Pattern extraction mathematically impossible without comprehensive dataset access
-
Context window limitations
- Modern AI systems: Finite context windows (8K-128K tokens)
- Human comprehension: Integration across years of accumulated knowledge
- Cross-domain transfer efficiency: Humans (10² examples) vs. pattern matching (10⁶ examples)
Criminal Intent: The Mathematics of Dataset Piracy Quantifiable extraction metrics*
+ Total extracted token count (billions-trillions)
+ Complete vs. partial work capture
+ Retention duration (permanent vs. ephemeral)
-
Intentionality factor
- Reading: Temporally constrained information absorption with natural decay functions
- Pirated training: Deliberate, persistent data capture designed for complete pattern extraction
- Forensic fingerprinting: Statistical signatures in model outputs revealing unauthorized distribution centroids
-
Technical protection circumvention
-
Systematic scraping operations exceeding fair use limitations
- Deliberate removal of copyright metadata and attribution
- Detection through embedding proximity analysis showing over-representation of protected materials
Legal and Mathematical Burden of Proof Information theory perspective*
+ Shannon entropy indicates minimum information requirements cannot be circumvented
+ Statistical approximation vs. structural understanding
+ Pattern matching mathematically requires access to complete datasets for value extraction
-
Fair use boundary violations
- Reading: Established legal doctrine with clear precedent
- Training: Quantifiably different usage patterns and data extraction methodologies
- Mathematical proof: Different operations performed on content with distinct technical requirements
This mathematical framing conclusively demonstrates that training pattern matching systems on intellectual property operates fundamentally differently from human reading, with distinct technical requirements, operational constraints, and forensically verifiable extraction signatures.
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Pattern Matching Systems: Powerful But DumbCore Concept: Pattern Recognition Without Understanding Mathematical foundation*: All systems operate through vector space mathematics
+ K-means clustering, vector databases, and AI coding tools share identical operational principles
+ Function by measuring distances between points in multi-dimensional space
+ No semantic understanding of identified patterns
-
Demystification framework: Understanding the mathematical simplicity reveals limitations
- Elementary vector mathematics underlies seemingly complex "AI" systems
- Pattern matching ≠ intelligence or comprehension
- Distance calculations between vectors form the fundamental operation
Three Cousins of Pattern Matching K-means clustering*
+ Groups data points based on proximity in vector space
+ Example: Clusters students by height/weight/age parameters
+ Creates Voronoi partitions around centroids
-
Vector databases
- Organizes and retrieves items based on similarity metrics
- Optimizes for fast nearest-neighbor discovery
- Fundamentally performs the same distance calculations as K-means
-
AI coding assistants
-
Suggests code based on statistical pattern similarity
- Predicts token sequences that match historical patterns
- No conceptual understanding of program semantics or execution
The Human Expert Requirement The labeling problem*
+ Computers identify patterns but cannot name or interpret them
+ Domain experts must contextualize clusters (e.g., "these are athletes")
+ Validation requires human judgment and domain knowledge
-
Recognition vs. understanding distinction
- Systems can group similar items without comprehending similarity basis
- Example: Color-based grouping (red/blue) vs. functional grouping (emergency vehicles)
- Pattern without interpretation is just mathematics, not intelligence
The Automation Paradox Critical contradiction in automation claims*
+ If systems are truly intelligent, why can't they:
- Automatically determine the optimal number of clusters?
- Self-label the identified groups?
- Validate their own code correctness?
+ Corporate behavior contradicts automation narratives (hiring developers)
-
Validation gap in practice
- Generated code appears correct but lacks correctness guarantees
- Similar to memorization without comprehension
- Example: Infrastructure-as-code generation requires human validation
The Human-Machine Partnership Reality Complementary capabilities*
+ Machines: Fast pattern discovery across massive datasets
+ Humans: Meaning, context, validation, and interpretation
+ Optimization of respective strengths rather than replacement
-
Future direction: Augmentation, not automation
- Systems should help humans interpret patterns
- True value emerges from human-machine collaboration
- Pattern recognition tools as accelerators for human judgment
Technical Insight: Simplicity Behind Complexity Implementation perspective*
+ K-means clustering can be implemented from scratch in an hour
+ Understanding the core mathematics demystifies "AI" claims
+ Pattern matching in multi-dimensional space ≠ artificial general intelligence
-
Practical applications
- Finding clusters in millions of data points (machine strength)
- Interpreting what those clusters mean (human strength)
- Combining strengths for optimal outcomes
This episode deconstructs the mathematical foundations of modern pattern matching systems to explain their capabilities and limitations, emphasizing that despite their power, they fundamentally lack understanding and require human expertise to derive meaningful value.
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K-means & Vector Databases: The Core ConnectionFundamental Similarity Same mathematical foundation* – both measure distances between points in space
+ K-means groups points based on closeness
+ Vector DBs find points closest to your query
+ Both convert real things into number coordinates
-
The "team captain" concept works for both
- K-means: Captains are centroids that lead teams of similar points
- Vector DBs: Often use similar "representative points" to organize search space
- Both try to minimize expensive distance calculations
How They Work Spatial thinking is key to both*
+ Turn objects into coordinates (height/weight/age → x/y/z points)
+ Closer points = more similar items
+ Both handle many dimensions (10s, 100s, or 1000s)
-
Distance measurement is the core operation
- Both calculate how far points are from each other
- Both can use different types of distance (straight-line, cosine, etc.)
- Speed comes from smart organization of points
Main Differences Purpose varies slightly*
+ K-means: "Put these into groups"
+ Vector DBs: "Find what's most like this"
-
Query behavior differs
- K-means: Iterates until stable groups form
- Vector DBs: Uses pre-organized data for instant answers
Real-World Examples Everyday applications*
+ "Similar products" on shopping sites
+ "Recommended songs" on music apps
+ "People you may know" on social media
-
Why they're powerful
- Turn hard-to-compare things (movies, songs, products) into comparable numbers
- Find patterns humans might miss
- Work well with huge amounts of data
Technical Connection Vector DBs often use K-means internally* + Many use K-means to organize their search space + Similar optimization strategies + Both are about organizing multi-dimensional space efficiently
Expert Knowledge Both need human expertise* + Computers find patterns but don't understand meaning + Experts needed to interpret results and design spaces + Domain knowledge helps explain why things are grouped together
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Finding Hidden Groups with K-means ClusteringWhat is Unsupervised Learning?Imagine you're given a big box of different toys, but they're all mixed up. Without anyone telling you how to sort them, you might naturally put the cars together, stuffed animals together, and blocks together. This is what computers do with unsupervised learning - they find patterns without being told what to look for.
K-means Clustering Explained SimplyK-means helps us find groups in data. Let's think about students in your class:
- Each student has a height (x)
- Each student has a weight (y)
- Each student has an age (z)
K-means helps us see if there are natural groups of similar students.
The Four Main Steps of K-means1. Picking Starting PointsFirst, we need to guess where our groups might be centered:
- We could randomly pick a few students as starting points
- Or use a smarter way called K-means++ that picks students who are different from each other
-
This is like picking team captains before choosing teams
-
Making TeamsNext, each student joins the team of the "captain" they're most similar to:
-
We measure how close each student is to each captain
- Students join the team of the closest captain
-
This makes temporary groups
-
Finding New CentersNow we find the middle of each team:
-
Calculate the average height of everyone on team 1
- Calculate the average weight of everyone on team 1
- Calculate the average age of everyone on team 1
- This average student becomes the new center for team 1
-
We do this for each team
-
Checking if We're DoneWe keep repeating steps 2 and 3 until the teams stop changing:
-
If no one switches teams, we're done
- If the centers barely move, we're done
- If we've tried enough times, we stop anyway
Why Starting Points MatterStarting with different captains can give us different final teams. This is actually helpful:
- We can try different starting points
- See which grouping makes the most sense
- Find patterns we might miss with just one try
Seeing Groups in 3DImagine plotting each student in the classroom:
- Height is how far up they are (x)
- Weight is how far right they are (y)
- Age is how far forward they are (z)
- The team/group is shown by color (like red, blue, or green)
The color acts like a fourth piece of information, showing which group each student belongs to. The computer finds these groups by looking at who's clustered together in the 3D space.
Why We Need Experts to Name the GroupsThe computer can find groups, but it doesn't know what they mean:
- It might find a group of tall, heavier, older students (maybe athletes?)
- It might find a group of shorter, lighter, younger students
- It might find a group of average height, weight students who vary in age
Only someone who understands students (like a teacher) can say:
- "Group 1 seems to be the basketball players"
- "Group 2 might be students who skipped a grade"
- "Group 3 looks like our regular students"
The computer finds the "what" (the groups), but experts explain the "why" and "so what" (what the groups mean and why they matter).
The Simple Math Behind K-meansK-means works by trying to make each student as close as possible to their team's center. The computer is trying to make this number as small as possible:
"The sum of how far each student is from their team's center"
It does this by going back and forth between:
- Assigning students to the closest team
- Moving the team center to the middle of the team
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Greedy Random Start Algorithms: From TSP to Daily LifeKey Algorithm ConceptsComputational Complexity Classifications Constant Time O(1)*: Runtime independent of input size (hash table lookups)
+ "The holy grail of algorithms" - execution time fixed regardless of problem size
+ Examples: Dictionary lookups, array indexing operations
-
Logarithmic Time O(log n): Runtime grows logarithmically
- Each doubling of input adds only constant time
- Divides problem space in half repeatedly
- Examples: Binary search, balanced tree operations
-
Linear Time O(n): Runtime grows proportionally with input
-
Most intuitive: One worker processes one item per hour → two items need two workers
- Examples: Array traversal, linear search
-
Quadratic O(n²), Cubic O(n³), Exponential O(2ⁿ): Increasingly worse runtime
-
Quadratic: Nested loops (bubble sort) - practical only for small datasets
- Cubic: Three nested loops - significant scaling problems
- Exponential: Runtime doubles with each input element - quickly intractable
-
Factorial Time O(n!): "Pathological case" with astronomical growth
-
Brute-force TSP solutions (all permutations)
- 4 cities = 24 operations; 10 cities = 3.6 million operations
- Fundamentally impractical beyond tiny inputs
Polynomial vs Non-Polynomial Time Polynomial Time (P)*: Algorithms with O(nᵏ) runtime where k is constant
+ O(n), O(n²), O(n³) are all polynomial
+ Considered "tractable" in complexity theory
-
Non-deterministic Polynomial Time (NP)
- Problems where solutions can be verified in polynomial time
- Example: "Is there a route shorter than length L?" can be quickly verified
- Encompasses both easy and hard problems
-
NP-Complete: Hardest problems in NP
-
All NP-complete problems are equivalent in difficulty
- If any NP-complete problem has polynomial solution, then P = NP
-
NP-Hard: At least as hard as NP-complete problems
-
Example: Finding shortest TSP tour vs. verifying if tour is shorter than L
The Traveling Salesman Problem (TSP)Problem Definition and Intractability Formal Definition: Find shortest possible route visiting each city exactly once and returning to origin * Computational Scaling*: Solution space grows factorially (n!)
+ 10 cities: 181,440 possible routes
+ 20 cities: 2.43×10¹⁸ routes (years of computation)
+ 50 cities: More possibilities than atoms in observable universe
-
Real-World Challenges:
- Distance metric violations (triangle inequality)
- Multi-dimensional constraints beyond pure distance
- Dynamic environment changes during execution
Greedy Random Start AlgorithmStandard Greedy Approach Mechanism: Always select nearest unvisited city * Time Complexity: O(n²) - dominated by nearest neighbor calculations * Memory Requirements: O(n) - tracking visited cities and current path * Key Weakness*: Extreme sensitivity to starting conditions + Gets trapped in local optima + Produces tours 15-25% longer than optimal solution + Visual metaphor: Getting stuck in a valley instead of reaching mountain bottom
Random Restart Enhancement Core Innovation: Multiple independent greedy searches from different random starting cities * Implementation Strategy: Run algorithm multiple times from random starting points, keep best result * Statistical Foundation: Each restart samples different region of solution space * Performance Improvement: Logarithmic improvement with iteration count * Implementation Advantages*: + Natural parallelization with minimal synchronization + Deterministic runtime regardless of problem instance + No parameter tuning required unlike metaheuristics
Real-World ApplicationsUrban Navigation Traffic Light Optimization*: Avoiding getting stuck at red lights + Greedy approach: When facing red light, turn right if that's green + Local optimum trap: Always choosing "shortest next segment" + Random restart equivalent: Testing multiple routes from different entry points + Implementation example: Navigation apps calculating multiple route options
Economic Decision Making Online Marketplace Selling*:
+ Problem: Setting optimal price without complete market information
+ Local optimum trap: Accepting first reasonable offer
+ Random restart approach: Testing multiple price points simultaneously across platforms
-
Job Search Optimization:
- Local optimum trap: Accepting maximum immediate salary without considering growth trajectory
- Random restart solution: Pursuing multiple different types of positions simultaneously
- Goal: Optimizing expected lifetime earnings vs. immediate compensation
Cognitive Strategy Key Insight: When stuck in complex decision processes, deliberately restart from different perspective * Implementation Heuristic: Test multiple approaches in parallel rather than optimizing a single path * Expected Performance*: 80-90% of optimal solution quality with 10-20% of exhaustive search effort
Core Principles Probabilistic Improvement: Multiple independent attempts increase likelihood of finding high-quality solutions * Bounded Rationality: Optimal strategy under computational constraints * Simplicity Advantage: Lower implementation complexity enables broader application * Cross-Domain Applicability*: Same mathematical principles apply across computational and human decision environments
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Hidden Features of Cargo: Podcast Episode NotesCustom Profiles & Build OptimizationCustom Compilation Profiles: Create targeted build configurations beyond dev/release
- [profile.quick-debug]opt-level = 1 # Some optimizationdebug = true # Keep debug symbols
- Usage: cargo build --profile quick-debug
- Perfect for debugging performance issues without full release build wait times
- Eliminates need for repeatedly specifying compiler flags manually
Profile-Guided Optimization (PGO): Data-driven performance enhancement
- Three-phase optimization workflow:# 1. Build instrumented versioncargo rustc --release -- -Cprofile-generate=./pgo-data# 2. Run with representative workloads to generate profile data./target/release/my-program --typical-workload# 3. Rebuild with optimization informed by collected datacargo rustc --release -- -Cprofile-use=./pgo-data
- Empirical performance gains: 5-30% improvement for CPU-bound applications
- Trains compiler to prioritize optimization of actual hot paths in your code
- Critical for data engineering and ML workloads where compute costs scale linearly
Workspace Management & OrganizationDependency Standardization: Centralized version control
-
Root Cargo.toml[workspace]members = ["app", "library-a", "library-b"][workspace.dependencies]
serde = "1.0"
tokio = { version = "1", features = ["full"] }
Member Cargo.toml[dependencies]
serde = { workspace = true }
+ Declare dependencies once, inherit everywhere (Rust 1.64+)
+ Single-point updates eliminate version inconsistencies
+ Drastically reduces maintenance overhead in multi-crate projects
Dependency Intelligence & AnalysisDependency Visualization: Comprehensive dependency graph insights
- cargo tree: Display complete dependency hierarchy
- cargo tree -i regex: Invert tree to trace what pulls in specific packages
- Essential for diagnosing dependency bloat and tracking transitive dependencies
Automatic Feature Unification: Transparent feature resolution
- If crate A needs tokio with rt-multi-thread and crate B needs tokio with macros
- Cargo automatically builds tokio with both features enabled
- Silently prevents runtime errors from missing features
- No manual configuration required—this happens by default
Dependency Overrides: Direct intervention in dependency graph
- [patch.crates-io]serde = { git = "https://github.com/serde-rs/serde" }
- Replace any dependency with alternate version without forking dependents
- Useful for testing fixes or working around upstream bugs
Build System Insights & PerformanceBuild Analysis: Objective diagnosis of compilation bottlenecks
- cargo build --timings: Generates HTML report visualizing:
- Per-crate compilation duration
- Parallelization efficiency
- Critical path analysis
- Identify high-impact targets for compilation optimization
Cross-Compilation Configuration: Target different architectures seamlessly
-
.cargo/config.toml[target.aarch64-unknown-linux-gnu]linker = "aarch64-linux-gnu-gcc"rustflags = ["-C", "target-feature=+crt-static"]
- Eliminates need for environment variables or wrapper scripts
- Particularly valuable for AWS Lambda ARM64 deployments
- Zero-configuration alternative: cargo zigbuild (leverages Zig compiler)
Testing Workflows & ProductivityTargeted Test Execution: Optimize testing efficiency
- Run ignored tests only: cargo test -- --ignored
- Mark resource-intensive tests with #[ignore] attribute
- Run selectively when needed vs. during routine testing
- Module-specific testing: cargo test module::submodule
- Pinpoint tests in specific code areas
- Critical for large projects where full test suite takes minutes
- Sequential execution: cargo test -- --test-threads=1
- Forces tests to run one at a time
- Essential for tests with shared state dependencies
Continuous Testing Automation: Eliminate manual test cycles
- Install automation tool: cargo install cargo-watch
- Continuous validation: cargo watch -x check -x clippy -x test
- Automatically runs validation suite on file changes
- Enables immediate feedback without manual test triggering
Advanced Compilation TechniquesLink-Time Optimization Refinement: Beyond boolean LTO settings
- [profile.release]lto = "thin" # Faster than "fat" LTO, nearly as effectivecodegen-units = 1 # Maximize optimization (at cost of build speed)
- "Thin" LTO provides most performance benefits with significantly faster compilation
Target-Specific CPU Optimization: Hardware-aware compilation
- [target.'cfg(target_arch = "x86_64")']rustflags = ["-C", "target-cpu=native"]
- Leverages specific CPU features of build/target machine
- Particularly effective for numeric/scientific computing workloads
Key Takeaways* Cargo offers Ferrari-like tuning capabilities beyond basic commands * Most powerful features require minimal configuration for maximum benefit * Performance optimization techniques can yield significant cost savings for compute-intensive workloads * The compound effect of these "hidden" features can dramatically improve developer experience and runtime efficiency
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Temporal Execution Framework: Unix AT Utility for AWS Resource OrchestrationCore MechanismsUnix at Utility Architecture* Kernel-level task scheduler implementing non-interactive execution semantics
* Persistence layer: /var/spool/at/ with priority queue implementation
* Differentiation from cron: single-execution vs. recurring execution patterns
* Syntax paradigm: echo 'command' | at HH:MM
Implementation DomainsEFS Rate-Limit Circumvention* API cooling period evasion methodology via scheduled execution
* Use case: Throughput mode transitions (bursting→elastic→provisioned)
* Constraints mitigation: Circumvention of AWS-imposed API rate-limiting
* Implementation syntax:
echo 'aws efs update-file-system --file-system-id fs-ID --throughput-mode elastic' | at 19:06 UTC
Spot Instance Lifecycle Management* Termination handling: Pre-interrupt cleanup processes * Resource reclamation: Scheduled snapshot/EBS preservation pre-reclamation * Cost optimization: Temporal spot requests during historical low-demand windows * User data mechanism: Integration of termination scheduling at instance initialization
Cross-Service Orchestration* Lambda-triggered operations: Scheduled resource modifications * EventBridge patterns: Timed event triggers for API invocation * State Manager associations: Configuration enforcement with temporal boundaries
Practical ApplicationsWorker Node Integration* Deployment contexts: EC2/ECS instances for orchestration centralization
* Cascading operation scheduling throughout distributed ecosystem
* Command simplicity: echo 'command' | at TIME
Resource Reference* Additional educational resources: pragmatic.ai/labs or PIML.com * Curriculum scope: REST, generative AI, cloud computing (equivalent to 3+ master's degrees)
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Assembly Language & WebAssembly: Evolutionary ParadigmsEpisode NotesI. Assembly Language: Foundational FrameworkOntological Definition
- Low-level symbolic representation of machine code instructions
- Minimalist abstraction layer above binary machine code (1s/0s)
- Human-readable mnemonics with 1:1 processor operation correspondence
Core Architectural Characteristics
- ISA-Specificity: Direct processor instruction set architecture mapping
- Memory Model: Direct register/memory location/IO port addressing
- Execution Paradigm: Sequential instruction execution with explicit flow control
- Abstraction Level: Minimal hardware abstraction; operations reflect CPU execution steps
Structural Components
- Mnemonics: Symbolic machine instruction representations (MOV, ADD, JMP)
- Operands: Registers, memory addresses, immediate values
- Directives: Non-compiled assembler instructions (.data, .text)
- Labels: Symbolic memory location references
II. WebAssembly: Theoretical FrameworkConceptual Architecture
- Binary instruction format for portable compilation targeting
- High-level language compilation target enabling near-native web platform performance
Architectural Divergence from Traditional Assembly
- Abstraction Layer: Virtual ISA designed for multi-target architecture translation
- Execution Model: Stack-based VM within memory-safe sandbox
- Memory Paradigm: Linear memory model with explicit bounds checking
- Type System: Static typing with validation guarantees
Implementation Taxonomy
- Binary Format: Compact encoding optimized for parsing efficiency
- Text Format (WAT): S-expression syntax for human-readable representation
- Module System: Self-contained execution units with explicit import/export interfaces
- Compilation Pipeline: High-level languages → LLVM IR → WebAssembly binary
III. Comparative AnalysisConceptual Continuity
- WebAssembly extends assembly principles via virtualization and standardization
- Preserves performance characteristics while introducing portability and security guarantees
Technical Divergences
- Execution Environment: Hardware CPU vs. Virtual Machine
- Memory Safety: Unconstrained memory access vs. Sandboxed linear memory
- Portability Paradigm: Architecture-specific vs. Architecture-neutral
IV. Evolutionary Significance* WebAssembly represents convergent evolution of assembly principles adapted to distributed computing * Maintains low-level performance characteristics while enabling cross-platform execution * Exemplifies incremental technological innovation building upon historical foundations
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STRACE: System Call Tracing Utility — Advanced Diagnostic AnalysisI. Introduction & Empirical Case StudyCase Study: Weta Digital Performance Optimization
- Diagnostic investigation of Python execution latency (~60s initialization delay)
- Root cause identification: Excessive filesystem I/O operations (103-104 redundant calls)
- Resolution implementation: Network call interception via wrapper scripts
- Performance outcome: Significant latency reduction through filesystem access optimization
II. Technical Foundation & Architectural ImplementationEtymological & Functional Classification
- Unix/Linux diagnostic utility implementing ptrace() syscall interface
- Primary function: Interception and recording of syscalls executed by processes
- Secondary function: Signal receipt and processing monitoring
- Evolutionary development: Iterative improvement of diagnostic capabilities
Implementation Architecture
- Kernel-level integration via ptrace() syscall
- Non-invasive process attachment methodology
- Runtime process monitoring without source code access requirement
III. Operational Parameters & Implementation MechanicsProcess Attachment Mechanism
- Direct PID targeting via ptrace() syscall interface
- Production-compatible diagnostic capabilities (non-destructive analysis)
- Long-running process compatibility (e.g., ML/AI training jobs, big data processing)
Execution Modalities
- Process hierarchy traversal (
-fflag for child process tracing) - Temporal analysis with microsecond precision (
-t,-r,-Tflags) - Statistical frequency analysis (
-cflag for syscall quantification) - Pattern-based filtering via regex implementation
Output Taxonomy
- Format specification:
syscall(args) = return_value [error_designation] - 64-bit/32-bit differentiation via ABI handlers
- Temporal annotation capabilities
IV. Advanced Analytical CapabilitiesPerformance Metrics
- Microsecond-precision timing for syscall latency evaluation
- Statistical aggregation of call frequencies
- Execution path profiling
I/O & System Interaction Analysis
- File descriptor tracking and comprehensive I/O operation monitoring
- Signal interception analysis with complete signal delivery visualization
- IPC mechanism examination (shared memory segments, semaphores, message queues)
V. Methodological Limitations & ConstraintsPerformance Impact Considerations
- Execution degradation (5-15×) from context switching overhead
- Temporal resolution limitations (microsecond precision)
- Non-deterministic elements: Race conditions & scheduling anomalies
- Heisenberg uncertainty principle manifestation: Observer effect on traced processes
VI. Ecosystem Position & Comparative AnalysisComplementary Diagnostic Tools
- ltrace: Library call tracing
- ftrace: Kernel function tracing
- perf: Performance counter analysis
Abstraction Level Differentiation
- Complementary to GDB (implementation level vs. code level analysis)
- Security implications: Privileged access requirement (CAP_SYS_PTRACE capability)
- Platform limitations: Disabled on certain proprietary systems (e.g., Apple OS)
VII. Production Application DomainsDiagnostic Applications
- Root cause analysis for syscall failure patterns
- Performance bottleneck identification
- Running process diagnosis without termination requirement
System Analysis
- Security auditing (privilege escalation & resource access monitoring)
- Black-box behavioral analysis of proprietary/binary software
- Containerization diagnostic capabilities (namespace boundary analysis)
Critical System Recovery
- Subprocess deadlock identification & resolution
- Non-destructive diagnostic intervention for long-running processes
- Recovery facilitation without system restart requirements
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Episode Notes: My Support Initiative for Federal Workers in TransitionEpisode OverviewIn this episode, I announce a special initiative from Pragmatic AI Labs to support federal workers who are currently in career transitions by providing them with free access to our educational platform. I explain how our technical training can help workers upskill and find new positions.
Key PointsAbout the Initiative* I'm offering free platform access to federal workers in transition through Pragmatic AI Labs * To apply, workers should email contact@paiml.com with: + Their LinkedIn profile + Email address + Previous government agency * Access will be granted "no questions asked" * I encourage listeners to share this opportunity with others in their network
About Pragmatic AI Labs* Our mission: "Democratize education and teach people cutting-edge skills" * We focus on teaching skills that are rapidly evolving and often too new for traditional university curricula * Our content has been featured at top universities including Duke, Northwestern, UC Davis, and UC Berkeley * Also featured on major educational platforms like Coursera and edX * We've built a custom platform with interactive labs and exclusive content
Technical Skills CoveredCloud Computing:
- Major providers: AWS, Azure, GCP
- Open source solutions: Kubernetes, containerization
Programming Languages:
- Strong focus on Rust (we have "potentially the most content on anywhere in the world")
- Python
- Emerging languages like Zig
Web Technologies:
- WebAssembly
- WebSockets
Artificial Intelligence:
- Practical approaches to generative AI
- Integration of cloud-based solutions (e.g., Amazon Bedrock)
- Working with local open-source models
My Philosophy and Approach* Our platform is specifically designed to "help people get jobs" * Content focused on practical skills for career advancement * Emphasis on teaching cutting-edge material that moves "too fast" for traditional education * We're committed to "helping humanity at scale"
Contact InformationEmail: contact@paiml.com
Closing MessageI conclude with a sincere offer to help as many transitioning federal workers as possible gain new skills and advance their careers.
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Dark Patterns in Recommendation Systems: Beyond Technical Capabilities1. Engagement Optimization PathologyMetric-Reality Misalignment: Recommendation engines optimize for engagement metrics (time-on-site, clicks, shares) rather than informational integrity or societal benefit
Emotional Gradient Exploitation: Mathematical reality shows emotional triggers (particularly negative ones) produce steeper engagement gradients
Business-Society KPI Divergence: Fundamental misalignment between profit-oriented optimization and societal needs for stability and truthful information
Algorithmic Asymmetry: Computational bias toward outrage-inducing content over nuanced critical thinking due to engagement differential
- Neurological Manipulation VectorsDopamine-Driven Feedback Loops: Recommendation systems engineer addictive patterns through variable-ratio reinforcement schedules
Temporal Manipulation: Strategic timing of notifications and content delivery optimized for behavioral conditioning
Stress Response Exploitation: Cortisol/adrenaline responses to inflammatory content create state-anchored memory formation
Attention Zero-Sum Game: Recommendation systems compete aggressively for finite human attention, creating resource depletion
-
Technical Architecture of ManipulationFilter Bubble Reinforcement
-
Vector similarity metrics inherently amplify confirmation bias
- N-dimensional vector space exploration increasingly constrained with each interaction
- Identity-reinforcing feedback loops create increasingly isolated information ecosystems
- Mathematical challenge: balancing cosine similarity with exploration entropy
Preference Falsification Amplification
- Supervised learning systems train on expressed behavior, not true preferences
- Engagement signals misinterpreted as value alignment
- ML systems cannot distinguish performative from authentic interaction
-
Training on behavior reinforces rather than corrects misinformation trends
-
Weaponization MethodologiesCoordinated Inauthentic Behavior (CIB)
-
Troll farms exploit algorithmic governance through computational propaganda
- Initial signal injection followed by organic amplification ("ignition-propagation" model)
- Cross-platform vector propagation creates resilient misinformation ecosystems
- Cost asymmetry: manipulation is orders of magnitude cheaper than defense
Algorithmic Vulnerability Exploitation
- Reverse-engineered recommendation systems enable targeted manipulation
- Content policy circumvention through semantic preservation with syntactic variation
- Time-based manipulation (coordinated bursts to trigger trending algorithms)
-
Exploiting engagement-maximizing distribution pathways
-
Documented Harm Case StudiesMyanmar/Facebook (2017-present)
-
Recommendation systems amplified anti-Rohingya content
- Algorithmic acceleration of ethnic dehumanization narratives
- Engagement-driven virality of violence-normalizing content
Radicalization Pathways
- YouTube's recommendation system demonstrated to create extremism pathways (2019 research)
- Vector similarity creates "ideological proximity bridges" between mainstream and extremist content
- Interest-based entry points (fitness, martial arts) serving as gateways to increasingly extreme ideological content
-
Absence of epistemological friction in recommendation transitions
-
Governance and Mitigation ChallengesScale-Induced Governance Failure
-
Content volume overwhelms human review capabilities
- Self-governance models demonstrably insufficient for harm prevention
- International regulatory fragmentation creates enforcement gaps
- Profit motive fundamentally misaligned with harm reduction
Potential Countermeasures
- Regulatory frameworks with significant penalties for algorithmic harm
- International cooperation on misinformation/disinformation prevention
- Treating algorithmic harm similar to environmental pollution (externalized costs)
-
Fundamental reconsideration of engagement-driven business models
-
Ethical Frameworks and Human RightsEthical Right to Truth: Information ecosystems should prioritize veracity over engagement
Freedom from Algorithmic Harm: Potential recognition of new digital rights in democratic societies
Accountability for Downstream Effects: Legal liability for real-world harm resulting from algorithmic amplification
Wealth Concentration Concerns: Connection between misinformation economies and extreme wealth inequality
- Future OutlookIncreased Regulatory Intervention: Forecast of stringent regulation, particularly from EU, Canada, UK, Australia, New Zealand
Digital Harm Paradigm Shift: Potential classification of certain recommendation practices as harmful like tobacco or environmental pollutants
Mobile Device Anti-Pattern: Possible societal reevaluation of constant connectivity models
Sovereignty Protection: Nations increasingly viewing algorithmic manipulation as national security concern
Note: This episode examines the societal implications of recommendation systems powered by vector databases discussed in our previous technical episode, with a focus on potential harms and governance challenges.
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Vector Databases for Recommendation Engines: Episode NotesIntroduction Vector databases power modern recommendation systems by finding relationships between entities in high-dimensional space * Unlike traditional databases that rely on exact matching, vector DBs excel at finding similar* items * Core application: discovering hidden relationships between products, content, or users to drive engagement
Key Technical ConceptsVector/Embedding: Numerical array that represents an entity in n-dimensional space
- Example: [0.2, 0.5, -0.1, 0.8] where each dimension represents a feature
- Similar entities have vectors that are close to each other mathematically
Similarity Metrics:
- Cosine Similarity: Measures angle between vectors (-1 to 1)
- Efficient computation: dot_product / (magnitude_a * magnitude_b)
- Intuitively: measures alignment regardless of vector magnitude
Search Algorithms:
- Exact Nearest Neighbor: Find K closest vectors (computationally expensive)
- Approximate Nearest Neighbor (ANN): Trades perfect accuracy for speed
- Computational complexity reduction: O(n) → O(log n) with specialized indexing
The "Five Whys" of Vector DatabasesTraditional databases can't find "similar" items
- Relational DBs excel at WHERE category = 'shoes'
- Can't efficiently answer "What's similar to this product?"
- Vector similarity enables fuzzy matching beyond exact attributes
Modern ML represents meaning as vectors
- Language models encode semantics in vector space
- Mathematical operations on vectors reveal hidden relationships
- Domain-specific features emerge from high-dimensional representations
Computation costs explode at scale
- Computing similarity across millions of products is compute-intensive
- Specialized indexing structures dramatically reduce computational complexity
- Vector DBs optimize specifically for high-dimensional similarity operations
Better recommendations drive business metrics
- Major e-commerce platforms attribute ~35% of revenue to recommendation engines
- Media platforms: 75%+ of content consumption comes from recommendations
- Small improvements in relevance directly impact bottom line
Continuous learning creates compounding advantage
- Each customer interaction refines the recommendation model
- Vector-based systems adapt without complete retraining
- Data advantages compound over time
Recommendation PatternsContent-Based Recommendations
- "Similar to what you're viewing now"
- Based purely on item feature vectors
- Key advantage: works with zero user history (solves cold start)
Collaborative Filtering via Vectors
- "Users like you also enjoyed..."
- User preference vectors derived from interaction history
- Item vectors derived from which users interact with them
Hybrid Approaches
- Combine content and collaborative signals
- Example: Item vectors + recency weighting + popularity bias
- Balance relevance with exploration for discovery
Implementation ConsiderationsMemory vs. Disk Tradeoffs
- In-memory for fastest performance (sub-millisecond latency)
- On-disk for larger vector collections
- Hybrid approaches for optimal performance/scale balance
Scaling Thresholds
- Exact search viable to ~100K vectors
- Approximate algorithms necessary beyond that threshold
- Distributed approaches for internet-scale applications
Emerging Technologies
- Rust-based vector databases (Qdrant) for performance-critical applications
- WebAssembly deployment for edge computing scenarios
- Specialized hardware acceleration (SIMD instructions)
Business ImpactE-commerce Applications
- Product recommendations drive 20-30% increase in cart size
- "Similar items" implementation with vector similarity
- Cross-category discovery through latent feature relationships
Content Platforms
- Increased engagement through personalized content discovery
- Reduced bounce rates with relevant recommendations
- Balanced exploration/exploitation for long-term engagement
Social Networks
- User similarity for community building and engagement
- Content discovery through user clustering
- Following recommendations based on interaction patterns
Technical ImplementationCore Operations
- insert(id, vector): Add entity vectors to database
- search_similar(query_vector, limit): Find K nearest neighbors
- batch_insert(vectors): Efficiently add multiple vectors
Similarity Computation
- fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 { let dot_product: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum(); let mag_a: f32 = a.iter().map(|x| x * x).sum::().sqrt(); let mag_b: f32 = b.iter().map(|x| x * x).sum::().sqrt(); if mag_a > 0.0 && mag_b > 0.0 { dot_product / (mag_a * mag_b) } else { 0.0 }}
Integration Touchpoints
- Embedding pipeline: Convert raw data to vectors
- Recommendation API: Query for similar items
- Feedback loop: Capture interactions to improve model
Practical AdviceStart Simple
- Begin with in-memory vector database for <100K items
- Implement basic "similar items" on product pages
- Validate with simple A/B test against current approach
Measure Impact
- Technical: Query latency, memory usage
- Business: Click-through rate, conversion lift
- User experience: Discovery satisfaction, session length
Scaling Strategy
- Start with exact search, move to approximate methods as needed
- Invest in quality of embeddings over algorithm sophistication
- Build feedback loop for continuous improvement
Key Takeaways* Vector databases fundamentally simplify recommendation architecture * Mathematical foundation: similarity = proximity in vector space * Strategic advantage comes from data quality and feedback loops * Modern implementation enables web-scale recommendation systems with minimal complexity * Rust-based solutions (like Qdrant) provide performance-optimized implementations
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The podcast notes effectively capture the key technical aspects of the WebSocket terminal implementation. The transcript explores how Rust's low-level control and memory management capabilities make it an ideal language for building high-performance terminal emulation over WebSockets.
What makes this implementation particularly powerful is the combination of Rust's ownership model with the PTY (pseudoterminal) abstraction. This allows for efficient binary data transfer without the overhead typically associated with scripting languages that require garbage collection.
The architecture demonstrates several advanced Rust patterns:
Zero-copy buffer management - Using Rust's ownership semantics to avoid redundant memory allocations when transferring terminal data
Async I/O with Tokio runtime - Leveraging Rust's powerful async/await capabilities to handle concurrent terminal sessions without blocking operations
Actor-based concurrency - Implementing the Actix actor model to maintain thread-safety across terminal session boundaries
FFI and syscall integration - Direct integration with Unix PTY facilities through Rust's foreign function interface
The containerization aspect complements Rust's performance characteristics by providing clean, reproducible environments with minimal overhead. This combination of Rust's performance with Docker's isolation creates a compelling architecture for browser-based terminals that rivals native applications in responsiveness.
For developers looking to understand practical applications of Rust's memory safety guarantees in real-world systems programming, this terminal implementation serves as an excellent case study of how ownership, borrowing, and zero-cost abstractions translate into tangible performance benefits.
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Silicon Valley's Anarchist Alternative: How Open Source Beats Monopolies and FascismCORE THESIS* Corporate-controlled tech resembles fascism in power concentration * Trillion-dollar monopolies create suboptimal outcomes for most people * Open source (Linux) as practical counter-model to corporate tech hegemony * Libertarian-socialist approach achieves both freedom and technical superiority
ECONOMIC CRITIQUE* Extreme wealth inequality
+ CEO compensation 1,000-10,000× worker pay
+ Wages stagnant while executive compensation grows exponentially
+ Wealth concentration enables government capture
-
Corporate monopoly patterns
- Planned obsolescence and artificial scarcity
- Printer ink market as price-gouging example
- VC-backed platforms convert existing services to rent-seeking models
- Regulatory capture preventing market correction
LIBERTARIAN-SOCIALISM FRAMEWORK* Distinct from authoritarian systems (communism)
+ Anti-bureaucratic
+ Anti-centralization
+ Pro-democratic control
+ Bottom-up vs. top-down decision-making
-
Key principles
- Federated/decentralized democratic control
- Worker control of workplaces and technical decisions
- Collective self-management vs. corporate/state domination
- Technical decisions made by practitioners, not executives
SPANISH ANARCHISM MODEL (1868-1939)* Largest anarchist movement in modern history * CNT (Confederación Nacional del Trabajo) + Anarcho-syndicalist union with 1M+ members + Worker solidarity without authoritarian control + Developed democratic workplace infrastructure + Successful until suppressed by fascism
LINUX/FOSS AS IMPLEMENTED MODEL* Technical embodiment of libertarian principles
+ Decentralized authority vs. hierarchical control
+ Voluntary contribution and association
+ Federated project structure
+ Collective infrastructure ownership
+ Meritocratic decision-making
-
Demonstrated superiority
- Powers 90%+ of global technical infrastructure
- Dominates top programming languages
- Microsoft's documented anti-Linux campaign (Halloween documents)
- Technical freedom enables innovation
SURVEILLANCE CAPITALISM MECHANISMS* Authoritarian control patterns + Mass data collection creating power asymmetries + Behavioral prediction products sold to bidders + Algorithmic manipulation of user behavior + Shadow profiles and unconsented data extraction + Digital enclosure of commons + Similar patterns to Stasi East Germany surveillance
PRACTICAL COOPERATIVE MODELS* Mondragón Corporation (Spain)
+ World's largest worker cooperative
+ 80,000+ employees across 100+ cooperatives
+ Democratic governance
+ Salary ratios capped at 6:1 (vs. 350:1 in US corps)
+ 60+ years of profitability
-
Spanish grocery cooperatives
- Millions of consumer-members
- 16,000+ worker-owners
- Lower consumer prices with better worker conditions
-
Success factors
-
Federated structure with local autonomy
- Inter-cooperation between entities
- Technical and democratic education
- Capital subordinated to labor, not vice versa
EXISTING LIBERTARIAN TECH ALTERNATIVES* Federated social media
+ Mastodon
+ ActivityPub
+ BlueSky
-
Community ownership models
- Municipal broadband
- Mesh networks
- Wikipedia
- Platform cooperatives
-
Privacy-respecting services
-
Signal (secure messaging)
- ProtonMail (encrypted email)
- Brave (privacy browser)
- DuckDuckGo (non-tracking search)
ACTION FRAMEWORK* Increase adoption of libertarian tech alternatives * Support open-source projects with resources and advocacy * Develop business models supporting democratic tech * Build human-centered, democratically controlled technology * Recognize that Linux/FOSS is not "communism" but its opposite - a non-authoritarian system supporting freedom
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EPISODE NOTES: AI CODING PATTERNS & DEFECT CORRELATIONSCore Thesis Key premise: Code churn patterns reveal developer archetypes with predictable quality outcomes * Novel insight: AI coding assistants exhibit statistical twins of "rogue developer" patterns (r=0.92) * Technical risk*: This correlation suggests potential widespread defect introduction in AI-augmented teams
Code Churn Research Background Definition: Measure of how frequently a file changes over time (adds, modifications, deletions) * Quality correlation: High relative churn strongly predicts defect density (~89% accuracy) * Measurement: Most predictive as ratio of churned LOC to total LOC * Research source*: Microsoft studies demonstrating relative churn as superior defect predictor
Developer Patterns AnalysisConsistent developer pattern:
- ~25% active ratio spread evenly (e.g., Linus Torvalds, Guido van Rossum)
- <10% relative churn with strategic, minimal changes
- 4-5× fewer defects than project average
- Key metric: Low M1 (Churned LOC/Total LOC)
Average developer pattern:
- 15-20% active ratio (sprint-aligned)
- Moderate churn (10-20%) with balanced feature/maintenance focus
- Follows team workflows and standards
- Key metric: Mid-range values across M1-M8
Junior developer pattern:
- Sporadic commit patterns with frequent gaps
- High relative churn (~30%) approaching danger threshold
- Experimental approach with frequent complete rewrites
- Key metric: Elevated M7 (Churned LOC/Deleted LOC)
Rogue developer pattern:
- Night/weekend work bursts with low consistency
- Very high relative churn (>35%)
- Working in isolation, avoiding team integration
- Key metric: Extreme M6 (Lines/Weeks of churn)
AI developer pattern:
- Spontaneous productivity bursts with zero continuity
- Extremely high output volume per contribution
- Significant code rewrites with inconsistent styling
- Key metric: Off-scale M8 (Lines worked on/Churn count)
- Critical finding: Statistical twin of rogue developer pattern
Technical ImplicationsExponential vs. linear development approaches:
- Continuous improvement requires linear, incremental changes
- Massive code bursts create defect debt regardless of source (human or AI)
CI/CD considerations:
- High churn + weak testing = "cargo cult DevOps"
- Particularly dangerous with dynamic languages (Python)
- Continuous improvement should decrease defect rates over time
Risk Mitigation Strategies1. Treat AI-generated code with same scrutiny as rogue developer contributions 2. Limit AI-generated code volume to minimize churn 3. Implement incremental changes rather than complete rewrites 4. Establish relative churn thresholds as quality gates 5. Pair AI contributions with consistent developer reviews
Key TakeawayThe optimal application of AI coding tools should mimic consistent developer patterns: minimal, targeted changes with low relative churn - not massive spontaneous productivity bursts that introduce hidden technical debt.
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The Automation Myth: Why Developer Jobs Aren't Going AwayCore Thesis* The "last mile problem" persistently prevents full automation * 90/10 rule: First 90% of automation is easy, last 10% proves exponentially harder * Tech monopolies strategically use automation narratives to influence markets and suppress labor * Genuine automation augments human capabilities rather than replacing humans entirely
Case Studies: Automation's Last Mile ProblemSelf-Checkout Systems* Implementation reality: Always requires human oversight (1 attendant per ~4-6 machines) * Failure modes demonstrate the 80/20 problem: + ID verification for age-restricted items + Weight discrepancies and unrecognized items + Coupon application and complex pricing + Unexpected technical errors * Modest efficiency gain (~30%) comes with hidden costs: + Increased shrinkage (theft) + Customer experience degradation + Higher maintenance requirements
Autonomous Vehicles* Billions invested with fundamental limitations still unsolved * Current capabilities work as assistive features only: + Highway driving assistance + Lane departure warnings + Automated parking * Technical barriers remain insurmountable for full autonomy: + Edge case handling (weather, construction, emergencies) + Local driving cultures and norms + Safety requirements (99.9% isn't good enough) * Used to prop up valuations despite lack of viable full automation path
Content Moderation* Persistent human dependency despite massive automation investment * Technical reality: AI flags content but humans make final decisions * Hidden workforce: Thousands of moderators reviewing flagged content * Ethical issues with outsourcing traumatic content review * Demonstrates that even with massive datasets, human judgment remains essential
Data Labeling Dependencies* Ironic paradox: AI systems require massive human-labeled training data * If AI were truly automating effectively, data labeling jobs would disappear * Quality AI requires increasingly specialized human labeling expertise * Shows fundamental dependency on human judgment persists
Developer Jobs: The DevOps RealityThe Code Generation Fallacy* Writing code isn't the bottleneck; sustainable improvement is * Bad code compounds logarithmically: + Initial development can appear exponentially productive + Technical debt creates logarithmic slowdown over time + System complexity eventually halts progress entirely * AI coding tools optimize for the wrong metric: + Focus on initial code generation, not long-term maintenance + Generate plausible but architecturally problematic solutions + Create hidden technical debt
Infrastructure as Code: The Canary in the Coal Mine* If automation worked, cloud infrastructure could be built via natural language * Critical limitations prevent this: + Security vulnerabilities from incomplete pattern recognition + Excessive verbosity required to specify all parameters + High-stakes failure consequences (account compromise, data loss) + Inability to reason about system-level architecture
The Chicken-and-Egg Paradox* If AI coding tools worked as advertised, they would recursively improve themselves * Reality check: AI tool companies hire more engineers, not fewer + OpenAI: 700+ engineers despite creating "automation" tools + Anthropic: Continuously hiring despite Claude's coding capabilities * No evidence of compounding productivity gains in AI development itself
Tech Monopolies & Market ManipulationStrategic Automation Narratives* Trillion-dollar tech companies benefit from automation hype: + Stock price inflation via future growth projections + Labor cost suppression and bargaining power reduction + Competitive moat-building (capital requirements) * Creates asymmetric power relationship with workers: + "Why unionize if your job will be automated?" + Encourages accepting lower compensation due to perceived job insecurity + Discourages smaller competitors from market entry
Hidden Human Dependencies* Tech giants maintain massive human workforces for supposedly "automated" systems: + Content moderation (15,000+ contractors) + Data labeling (100,000+ global workers) + Quality assurance and oversight * Cost structure deliberately obscured in financial reporting * True economics of "AI systems" include significant hidden human labor costs
Developer Career StrategyFocus on Augmentation, Not Replacement* Use automation tools to handle routine aspects of development * Redirect energy toward higher-value activities: + System architecture and integration + Security and performance optimization + Business domain expertise
Skill Development Priorities* Learn modern compiled languages with stronger guarantees (e.g., Rust) * Develop expertise in system efficiency: + Energy and computational optimization + Cost efficiency at scale + Security hardening
Professional Positioning* Recognize automation narratives as potential labor suppression tactics * Focus on deepening technical capabilities rather than breadth * Understand the fundamental value of human judgment in software engineering
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Maslow's Hierarchy of Logging - Podcast Episode NotesCore Concept* Logging exists on a maturity spectrum similar to Maslow's hierarchy of needs * Software teams must address fundamental logging requirements before advancing to sophisticated observability
Level 1: Print Statements Definition: Raw output statements (printf, console.log) for basic debugging * Limitations: + Creates ephemeral debugging artifacts (add prints → fix issue → delete prints → similar bug reappears → repeat) + Zero runtime configuration (requires code changes) + No standardization (format, levels, destinations) + Visibility limited to execution duration + Cannot filter, aggregate, or analyze effectively * Examples*: Python print(), JavaScript console.log(), Java System.out.println()
Level 2: Logging Libraries Definition: Structured logging with configurable severity levels * Benefits: + Runtime-configurable verbosity without code changes + Preserves debugging context across debugging sessions + Enables strategic log retention rather than deletion * Key Capabilities: + Log levels (debug, info, warning, error, exception) + Production vs. development logging strategies + Exception tracking and monitoring * Sub-levels: + Unstructured logs (harder to query, requires pattern matching) + Structured logs (JSON-based, enables key-value querying) + Enables metrics dashboards, counts, alerts * Examples*: Python logging module, Rust log crate, Winston (JS), Log4j (Java)
Level 3: Tracing Definition: Tracks execution paths through code with unique trace IDs * Key Capabilities: + Captures method entry/exit points with precise timing data + Performance profiling with lower overhead than traditional profilers + Hotspot identification for optimization targets * Benefits: + Provides execution context and sequential flow visualization + Enables detailed performance analysis in production * Examples*: OpenTelemetry (vendor-neutral), Jaeger, Zipkin
Level 4: Distributed Tracing Definition: Propagates trace context across process and service boundaries * Use Case: Essential for microservices and serverless architectures (5-500+ transactions across services) * Key Capabilities: + Correlates requests spanning multiple services/functions + Visualizes end-to-end request flow through complex architectures + Identifies cross-service latency and bottlenecks + Maps service dependencies + Implements sampling strategies to reduce overhead * Examples*: OpenTelemetry Collector, Grafana Tempo, Jaeger (distributed deployment)
Level 5: Observability Definition: Unified approach combining logs, metrics, and traces * Context: Beyond application traces - includes system-level metrics (CPU, memory, disk I/O, network) * Key Capabilities: + Unknown-unknown detection (vs. monitoring known-knowns) + High-cardinality data collection for complex system states + Real-time analytics with anomaly detection + Event correlation across infrastructure, applications, and business processes + Holistic system visibility with drill-down capabilities * Analogy: Like a vehicle dashboard showing overall status with ability to inspect specific components * Examples*: + Grafana + Prometheus + Loki stack + ELK Stack (Elasticsearch, Logstash, Kibana) + OpenTelemetry with visualization backends
Implementation Strategies Progressive adoption: Start with logging fundamentals, then build up * Future-proofing: Design with next level in mind * Tool integration: Select tools that work well together * Team capabilities*: Match observability strategy to team skills and needs
Key Takeaway* Print debugging is survival mode; mature production systems require observability * Each level builds on previous capabilities, adding context and visibility * Effective production monitoring requires progression through all levels
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TCP vs UDP: Foundational Network ProtocolsProtocol FundamentalsTCP (Transmission Control Protocol) Connection-oriented: Requires handshake establishment * Reliable delivery: Uses acknowledgments and packet retransmission * Ordered packets: Maintains exact sequence order * Header overhead: 20-60 bytes (≈20% additional overhead) * Technical implementation*: + Three-way handshake (SYN → SYN-ACK → ACK) + Flow control via sliding window mechanism + Congestion control algorithms + Segment sequencing with reordering capability + Full-duplex operation
UDP (User Datagram Protocol) Connectionless: "Fire-and-forget" transmission model * Best-effort delivery: No delivery guarantees * No packet ordering: Packets arrive independently * Minimal overhead: 8-byte header (≈4% overhead) * Technical implementation*: + Stateless packet delivery + No connection establishment or termination phases + No congestion or flow control mechanisms + Basic integrity verification via checksum + Fixed header structure
Real-World ApplicationsTCP-Optimized Use Cases Web browsers (Chrome, Firefox, Safari) - HTTP/HTTPS traffic * Email clients (Outlook, Gmail) * File transfer tools (Filezilla, WinSCP) * Database clients (MySQL Workbench) * Remote desktop applications (RDP) * Messaging platforms (Slack, Discord text) * Common requirement*: Complete, ordered data delivery
UDP-Optimized Use Cases Online games (Fortnite, Call of Duty) - real-time movement data * Video conferencing (Zoom, Google Meet) - audio/video streams * Streaming services (Netflix, YouTube) * VoIP applications * DNS resolvers * IoT devices and telemetry * Common requirement*: Time-sensitive data where partial loss is acceptable
Performance CharacteristicsTCP Performance Profile Higher latency: Due to handshakes and acknowledgments * Reliable throughput: Stable performance on reliable connections * Connection state limits: Impacts concurrent connection scaling * Best for*: Applications where complete data integrity outweighs latency concerns
UDP Performance Profile Lower latency: Minimal protocol overhead * High throughput potential: But vulnerable to network congestion * Excellent scalability: Particularly for broadcast/multicast scenarios * Best for*: Real-time applications where occasional data loss is preferable to waiting
Implementation ConsiderationsWhen to Choose TCP* Data integrity is mission-critical * Complete file transfer verification required * Operating in unpredictable or high-loss networks * Application can tolerate some latency overhead
When to Choose UDP* Real-time performance requirements * Partial data loss is acceptable * Low latency is critical to application functionality * Application implements its own reliability layer if needed * Multicast/broadcast functionality required
Protocol Evolution* TCP variants: TCP Fast Open, Multipath TCP, QUIC (Google's HTTP/3) * UDP enhancements: DTLS (TLS-like security), UDP-Lite (partial checksums) * Hybrid approaches emerging in modern protocol design
Practical Implications* Protocol selection fundamentally impacts application behavior * Understanding the differences critical for debugging network issues * Low-level implementation possible in systems languages like Rust * Services may utilize both protocols for different components
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Tracing vs. Logging in Production SystemsCore Concepts Logging & Tracing = "Data Science for Production Software"* + Essential for understanding system behavior at scale + Provides insights when services are invoked millions of times monthly + Often overlooked by beginners focused solely on functionality
Fundamental Differences Logging*
+ Point-in-time event records
+ Captures discrete events without inherent relationships
+ Traditionally unstructured/semi-structured text
+ Stateless: each log line exists independently
+ Examples: errors, state changes, transactions
-
Tracing
- Request-scoped observation across system boundaries
- Maps relationships between operations with timing data
- Contains parent-child hierarchies
- Stateful: spans relate to each other within context
- Examples: end-to-end request flows, cross-service dependencies
Technical Implementation Logging Implementation*
+ Levels: ERROR, WARN, INFO, DEBUG
+ Manual context addition (critical for meaningful analysis)
+ Storage optimized for text search and pattern matching
+ Advantage: simplicity, low overhead, toggleable verbosity
-
Tracing Implementation
- Spans represent operations with start/end times
- Context propagation via headers or messaging metadata
- Sampling decisions at trace inception
- Storage optimized for causal graphs and timing analysis
- Higher network overhead and integration complexity
Use Cases When to Use Logging*
+ Component-specific debugging
+ Audit trail requirements
+ Simple deployment architectures
+ Resource-constrained environments
-
When to Use Tracing
- Performance bottleneck identification
- Distributed transaction monitoring
- Root cause analysis across service boundaries
- Microservice and serverless architectures
Modern Convergence Structured Logging*
+ JSON formats enable better analysis and metrics generation
+ Correlation IDs link related events
-
Unified Observability
- OpenTelemetry combines metrics, logs, and traces
- Context propagation standardization
- Multiple views of system behavior (CPU, logs, transaction flow)
Rust Implementation Logging Foundation*
+ `log` crate: de facto standard
+ Log macros: `error!`, `warn!`, `info!`, `debug!`, `trace!`
+ Environmental configuration for level toggling
-
Tracing Infrastructure
tracingcrate for next-generation instrumentationinstrument,span!,event!macros- Subscriber model for telemetry processing
- Native integration with async ecosystem (Tokio)
- Web framework support (Actix, etc.)
Key Implementation Consideration Transaction IDs* + Critical for linking events across distributed services + Must span entire request lifecycle + Enables correlation of multi-step operations
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The Rise of Expertise Inequality in AIKey Points Similar to income inequality growth since 1980, we may now be witnessing the emergence of expertise inequality* with AI
Problem: Automation Claims Lack Nuance* Claims about "automating coders" or eliminating software developers oversimplify complex realities * Example: AWS deployment decisions require expertise + Multiple compute options (EC2, Lambda, ECS Fargate, EKS, Elastic Beanstalk) + Each option has significant tradeoffs and use cases + Surface-level AI answers lack depth for informed decision-making
Expertise Inequality DynamicsExperts Will Thrive* Deep experts can leverage AI effectively * They understand fundamental tradeoffs (e.g., compiled vs scripting languages) * Can make optimized choices (e.g., Rust for Lambda functions) * Know exactly what questions to ask AI systems
Beginners Will Struggle* Lack domain knowledge to evaluate AI suggestions * Don't understand fundamental distinctions (website vs web service) * Cannot properly prompt AI systems due to knowledge gaps
Organizational Impact Dysfunctional organizations at risk* + HIPAA-driven (High-Paid Person's Opinion) + University systems + Corporate bureaucracies * Expert individuals may outperform entire teams * Experts with AI might deliver in one day what organizations take a full year to complete
AI Reality Check* Current generative AI is fundamentally: 1. Enhanced Stack Overflow 2. Fancy search engine 3. Pattern recognition system * Not truly "intelligent" - builds on existing information services * Will reach perfect competition as technologies standardize * Open source solutions rapidly approaching commercial offerings
Future Predictions1. Experts become increasingly valuable 2. Beginners face decreased demand 3. Dysfunctional organizations accelerate toward failure 4. Expertise inequality may become as concerning as income inequality
ConclusionThe AI revolution isn't replacing expertise - it's making it more valuable than ever.
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EU Cloud Sovereignty & Open Source AlternativesMarket Overview Current EU Cloud Market Share* + AWS: ~33% market share (Frankfurt, Ireland, Paris regions) + Microsoft Azure: ~25% market share + Google Cloud Platform: ~10% market share + OVHcloud: ~5% market share (largest EU-headquartered provider)
EU Sovereign Cloud ProvidersFull-Stack European SolutionsOVHcloud (France)
- 33 datacenters across 4 continents, 400K+ servers
- Vertical integration: custom server manufacturing in Roubaix
- Proprietary Linux-based virtualization layer
- Self-built European fiber backbone
- In-house distributed storage system (non-S3 compatible)
Scaleway (France)
- Growing integration with French AI companies (e.g., Mistral)
- Custom hypervisor and management plane
- ARM-based server architectures
- Datacenters in France, Poland, Netherlands
- Growing rapidly in SME/startup segment
Hetzner (Germany)
- Bare metal-focused infrastructure
- Proprietary virtualization layer
- 100% European datacenters (Germany, Finland)
- Custom DDoS protection systems designed in Germany
- Complete physical/logical isolation from US networks
Other European Providers Deutsche Telekom/T-Systems (Germany) * Orange Business Services (France) * SAP* (Germany)
Leading Open Source Cloud PlatformsTier 1OpenStack
- Most mature, enterprise-ready open source cloud platform
- Comprehensive IaaS functionality with modular architecture
- Key components: Nova (compute), Swift (object storage), Neutron (networking)
- Strong adoption in telecommunications, research, government sectors
Kubernetes
- "Cloud in a box" container orchestration platform
- Not a complete cloud solution but foundational component
- Cross-cloud compatibility (GKE, EKS, AKS)
- Key features: exceptional scalability, self-healing, declarative configuration
- Facilitates workload portability between cloud providers
Tier 2Apache CloudStack
- Enterprise-grade IaaS platform
- Single management server architecture
- Straightforward installation, less architectural flexibility
- Mature and stable for production
OpenNebula
- Lightweight virtualization management
- Lower resource requirements than OpenStack
- Strong integration with VMware and KVM environments
Emerging PlatformsRancher/K3s
- Lightweight Kubernetes distribution
- Optimized for edge computing
- Simplified binary deployment model
- Growing edge computing ecosystem
OKD (OpenShift Kubernetes Distribution)
- Upstream project for Red Hat OpenShift
- Developer-focused capabilities on Kubernetes
Geopolitical & Strategic Context* Growing US-EU tension creating market opportunity for European cloud sovereignty * European emphasis on data privacy, rights-based innovation, and technological independence * Potential bifurcation between US and European technology ecosystems * Rising concern about Big Tech's influence on governance and sovereignty * European cloud providers positioned as alternatives emphasizing human rights, privacy
Technical Independence Challenges* Processor architecture dependencies (Intel/AMD dominance) * European Processor Initiative and SiPearl developing EU alternatives * Full software stack independence remains aspirational * Network equipment supply chain complexities
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European Digital Sovereignty: Breaking Tech DependencyEpisode NotesHeterodox Economic Foundations (00:00-02:46)* Current economic context: Income inequality at historic levels (worse than pre-French Revolution) * Problems with GDP as primary metric: + Masks inequality when wealth is concentrated + Fails to measure human wellbeing + American example: majority living paycheck-to-paycheck despite GDP growth * Alternative metrics: + Human dignity quantification + Planetary health indicators + Commons-based resource management + Care work valuation (teaching, healthcare, social work) + Multi-dimensional inequality measurement * Practical examples: + Life expectancy as key metric (EU/Japan vs US differences) + Education quality and accessibility + Democratic participation + Income distribution
Digital Infrastructure Autonomy (02:46-03:18)* European cloud infrastructure development (GAIA-X) * Open-source technology adoption in public institutions * Local semiconductor production capacity * Network infrastructure without US-controlled chokepoints
Income Redistribution via Tech Regulation (03:18-03:53)* Digital services taxation models * Graduated taxation based on market concentration * Labor share requirements through tax incentives * SME ecosystem development through regulatory frameworks
Health Data Sovereignty (03:53-04:29)* Patient data localization requirements * Indigenous medical technology development * European-controlled health datasets for AI training * Contrasting social healthcare vs. capitalistic healthcare models
Agricultural Technology Independence (04:29-04:53)* European research-driven precision farming * Farm management systems with European values (cooperative models) * Rural connectivity self-sufficiency for smart farming
Information Ecosystem Control (04:53-05:33)* European content moderation standards * Concerns about American platforms' rule changes * Public funding for quality news content * Taxation mechanisms on disinformation spread
Democratic Technology Governance (05:33-06:17)* Algorithmic impact assessment frameworks * Evaluating offline harm potential * Digital rights enforcement mechanisms * Countering extremist content proliferation
Mobility Data Sovereignty (06:17-06:33)* Public transportation data ownership by European cities * Vehicle data localization requirements * European component requirements for autonomous vehicles
Taxation Technology Independence (06:33-06:48)* Tax incentives for European tech adoption * Penalties for dependence on US vendors * Strategic technology sector preferences
Climate Technology Self-Sufficiency (06:48-07:03)* Renewable energy management software * Carbon accounting tools * Prioritizing climate technology in economic planning
Conclusion: Competing Through Rights-Based Innovation (07:03-10:36)* Critique of American outcomes despite GDP growth: + Declining life expectancy + Healthcare bankruptcy + Gun violence * European competitive advantage through: + Human rights prioritization + Environmental protection + Deterministic technology development + Constructive vs. extractive economic models * Potential to attract global talent seeking better quality of life * Reframing "overregulation" criticisms as human rights defense * Building rather than extracting as the European model
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WebAssembly Core Concepts - Episode NotesIntroduction [00:00-00:14]* Overview of episode focus: WebAssembly core concepts * Structure: definition, purpose, implementation pathways
Fundamental Definition [00:14-00:38]* Low-level binary instruction format for stack-based virtual machine * Designed as compilation target for high-level languages * Enables client/server application deployment * Near-native performance execution capabilities * Speed as primary advantage
Technical Architecture [00:38-01:01]* Binary format with deterministic execution model * Structured control flow with validation constraints * Linear memory model with protected execution * Static type system for function safety
Runtime Characteristics [01:01-01:33]* Execution in structured stack machine environment * Processes structured control flow (blocks, loops, branches) * Memory-safe sandboxed execution environment * Static validation for consistent behavior guarantees
Compilation Pipeline [01:33-02:01]* Accepts diverse high-level language inputs (C++, Rust) * Implements efficient compilation strategies * Generates optimized binary format output * Maintains debugging information through source maps
Architectural Components [02:01-02:50]Virtual Machine Integration:
- Operates alongside JavaScript in browser
- Enables distinct code execution pathways
- Maintains interoperability between runtimes
Binary Format Implementation:
- Compact format designed for low latency
- Near-native execution performance
- Instruction sequences optimized for modern processors
Memory Model:
- Linear memory through ArrayBuffer
- Low-level memory access
- Maintains browser sandbox security
Core Technical Components [02:50-03:53]Module System:
- Fundamental compilation unit
- Stateless design for cross-context sharing
- Explicit import/export interfaces
- Deterministic initialization semantics
Memory Management:
- Resizable ArrayBuffer for linear memory operations
- Bounds-checked memory access
- Direct binary data manipulation
- Memory isolation between instances
Table Architecture:
- Stores reference types not representable as raw bytes
- Implements dynamic dispatch
- Supports function reference management
- Enables indirect call operations
Integration Pathways [03:53-04:47]C/C++ Development:
- Emscripten toolchain
- LLVM backend optimizations
- JavaScript interface code generation
- DOM access through JavaScript bindings
Rust Development:
- Native WebAssembly target support
- wasm-bindgen for JavaScript interop
- Direct wasm-pack integration
- Zero-cost abstractions
AssemblyScript:
- TypeScript-like development experience
- Strict typing requirements
- Direct WebAssembly compilation
- Familiar tooling compatibility
Performance Characteristics [04:47-05:30]Execution Efficiency:
- Near-native execution speeds
- Optimized instruction sequences
- Reduced parsing and compilation overhead
- Consistent performance profiles
Memory Efficiency:
- Direct memory manipulation
- Reduced garbage collection overhead
- Optimized binary data operations
- Predictable memory patterns
Security Implementation [05:30-05:53]* Sandboxed execution * Browser security policy enforcement * Memory isolation * Same-origin restrictions * Controlled external access
Web Platform Integration [05:53-06:20]JavaScript Interoperability:
- Bidirectional function calls
- Primitive data type exchange
- Structured data marshaling
- Synchronous operation capability
DOM Integration:
- DOM access through JavaScript bridges
- Event handling mechanisms
- Web API support
- Browser compatibility
Development Toolchain [06:20-06:52]Compilation Targets:
- Multiple source language support
- Optimization pipelines
- Debugging capabilities
- Tooling integrations
Development Workflow:
- Modular development patterns
- Testing frameworks
- Performance profiling tools
- Deployment optimizations
Future Development [06:52-07:10]* Direct DOM access capabilities * Enhanced garbage collection * Improved debugging features * Expanded language support * Platform evolution
Resources [07:10-07:40]* Mozilla Developer Network (developer.mozilla.org) * WebAssembly concepts documentation * Web API implementation details * Mozilla's official curriculum
Production Notes* Total Duration: ~7:40 * Key visualization opportunities: + Stack-based VM architecture diagram + Memory model illustration + Language compilation pathways + Performance comparison graphs
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The End of Moore's Law and the Future of Computing PerformanceThe Automobile Industry Parallel* 1960s: Focus on power over efficiency (muscle cars, gas guzzlers) * Evolution through Japanese efficiency, turbocharging, to electric vehicles * Similar pattern now happening in computing
The Python Performance Crisis* Matrix multiplication example: 7 hours vs 0.5 seconds * 60,000x performance difference through optimization * Demonstrates massive inefficiencies in modern languages * Industry was misled by Moore's Law into deprioritizing performance
Performance Improvement Hierarchy1. Language Choice Improvements:
* Java: 11x faster than Python
* C: 50x faster than Python
* Why stop at C-level performance?
-
Additional Optimization Layers:
- Parallel loops: 366x speedup
- Parallel divide and conquer
- Vectorization
- Chip-specific features
The New Reality in 2025* Moore's Law's automatic performance gains are gone * LLMs make code generation easier but not necessarily better * Need experts who understand performance optimization * Pushing for "faster than C" as the new standard
Future Directions* Modern compiled languages gaining attention (Rust, Go, Zig) * Example: 16KB Zig web server in Docker * Rethinking architectures: + Microservices with tiny containers + WebAssembly over JavaScript + Performance-first design
Key Paradigm Shifts* Developer time no longer prioritized over runtime * Production code should never be slower than C * Single-stack ownership enables optimization * Need for coordinated improvement across: + Language design + Algorithms + Hardware architecture
Looking Forward* Shift from interpreted to modern compiled languages * Performance engineering becoming critical skill * Domain-specific hardware acceleration * Integrated approach to performance optimization
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Technical Architecture for Digital IndependenceCore ConceptSmartphones represent a monolithic architecture that needs to be broken down into microservices for better digital independence.
Authentication Strategy* Hardware security keys (YubiKey) replace mobile authenticators + USB-C insertion with button press + More convenient than SMS/app-based 2FA + Requires backup key strategy * Offline authentication options + Local encrypted SQLite password database + Air-gapped systems + Backup protocols
Device Distribution Architecture* Core Components: + Dumbphone/flip phone for basic communication + Offline GPS device with downloadable maps + Utility Android tablet ($50-100) for specific apps + Linux workstation for development * Implementation: + SIM transfer protocols between carriers + Data isolation techniques + Offline-first approach + Device-specific use cases
Data Strategy* Cloud Migration: + iCloud data extraction + Local storage solutions + Privacy-focused sync services + Encrypted remote storage with rsync * Linux Migration: + Open source advantages + Reduced system overhead + No commercial spyware + Powers 90% of global infrastructure
Network Architecture* Distributed Connectivity: + Pay-as-you-go hotspots + Minimal data plan requirements + Improved security through isolation * Use Cases: + Offline maps for navigation + Batch downloading for podcasts + Home network sync for updates + Garage WiFi for car updates
Cost Benefits* Standard smartphone setup: ~$5,000/year + iPhone upgrades + Data plans + Cloud services * Microservices approach: + Significantly reduced costs + Better concentration + Improved control + Enhanced privacy
Key TakeawaySoftware engineering perspective suggests breaking monolithic mobile systems into optimized, offline-first microservices for better functionality and reduced dependency.
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Feynman's Wisdom Applied to AI LearningBackground* Feynman helped create atomic bomb and investigated Challenger disaster * Challenger investigation revealed bureaucracy prioritized power over engineering solutions * Two key phrases found on his blackboard at death: + "What I cannot create, I do not understand" + "Know how to solve every problem that has been solved"
Applied to Pragmatic AI Labs CoursesWhat I Cannot Create* Build token processor before using Bedrock * Implement basic embeddings before production models * Write minimal GPU kernels before CUDA libraries * Create raw model inference before frameworks * Deploy manual servers before cloud services
Learning Solved Problems* Study successful AI architectures * Reimplement ML papers * Analyze deployment patterns * Master optimization techniques * Learn security boundaries
Implementation Strategy* Build core concepts from scratch * Move to frameworks only after raw implementation * Break systems intentionally to understand them * Build instead of memorize * Ex: Build S3 bucket/Lambda vs. memorizing for certification
Platform Support* Interactive labs available * Source code starter kits * Multiple languages: Python, Rust, SQL, Bash, Zig * Focus on first principles * Community-driven learning approach
Key TakeawayFocus on understanding through creation, leveraging proven solutions as foundation for innovation.
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The Rise of Micro-Containers: When Less is MorePodcast Episode Notes
Opening (0:00 - 0:40)* Introduction to micro-containers: containers under 100KB * Contrast with typical Python containers (5GB+) * Languages enabling micro-containers: Rust, Zig, Go
Zig Code Example (0:40 - 1:10)
// 16KB HTTP server exampleconst std = @import("std");pub fn main() !void { var server = try std.net.StreamServer.init(.{}); defer server.deinit(); try server.listen(try std.net.Address.parseIp("0.0.0.0", 8080)); while (true) { const conn = try server.accept(); try handleRequest(conn); }}
Key Use Cases Discussed (1:10 - 5:55)1. Edge IoT (1:14)* ESP32 with 4MB flash constraints
* Temperature sensor example: 60KB total with MQTT
* A/B firmware updates within 2MB limit
- WASM Integration (2:37)* Millisecond-loading micro-frontends
- Component isolation per container
-
Zero initialization overhead for routing
-
Serverless Performance (3:11)* Traditional: 300ms cold start
- Micro-container: 50ms start
-
Direct memory mapping benefits
-
Security Benefits (3:38)* No shell = no injection surface
- Single binary audit scope
-
Zero trust architecture approach
-
Embedded Linux (3:58)* Raspberry Pi (512MB RAM) use case
- 50+ concurrent services under 50KB each
-
Home automation applications
-
CI/CD Improvements (4:19)* Base image: 300MB → 20KB
- 10-15x faster pipelines
-
Reduced bandwidth costs
-
Mesh Networks (4:40)* P2P container distribution
- Minimal bandwidth requirements
-
Resilient to network partitions
-
FPGA Integration (5:05)* Bitstream wrapper containers
- Algorithm switching efficiency
-
Hardware-software bridge
-
Unikernel Comparison (5:30)* Container vs specialized OS
- Security model differences
-
Performance considerations
-
Cost Analysis (5:41)* Lambda container: 140MB vs 50KB
- 2800x storage reduction
- Cold start cost implications
Closing Thoughts (6:06 - 7:21)* Historical context: Solaris containers in 2000s * New paradigm: thinking in kilobytes * Scratch container benefits * Future of minimal containerization
Technical Implementation Note
// Example of stripped Zig binary for scratch containerconst builtin = @import("builtin");pub fn main() void { // No stdlib import needed asm volatile ("syscall" :: [syscall] "{rax}" (1), // write [fd] "{rdi}" (1), // stdout [buf] "{rsi}" ("ok\n"), [count] "{rdx}" (3) );}
Episode Duration: 7:21
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Software Development Job Market in 2025: Challenges & OpportunitiesMarket Downturn AnalysisInterest Rate Impact* Fed rates rose from ~0% to 5%, ending era of "free money" for VCs * Job postings dropped to COVID-era levels (index ~60) from 2022 peak (index ~220) * High rates reducing startup funding and venture capital activity
Monopoly Effects* Big tech companies engaged in defensive hiring to block competitors * Market distortions from trillion-dollar companies with limited competition * Regulatory failure to break up tech monopolies contributed to hiring instability
AI Impact Reality Check* LLMs primarily boost senior developer productivity * No evidence of AI replacing programming jobs * Tool comparison: Similar to Stack Overflow or programming books * Benefits experienced developers most; requires deep domain knowledge
Economic Headwinds* Tariff threats driving continued inflation * Government workforce reductions adding job seekers to market * AI investment showing weak ROI * Growing competition in AI space (OpenAI, Anthropic, Google, etc.) reducing profit potential
OpportunitiesValue-Based Skills* Focus on cost reduction and efficiency * Build solutions 100-1000x cheaper * Target performance-critical systems * Learn Rust for system optimization
Independent Business* Solo companies more viable with: + LLM assistance for faster development + Cloud infrastructure availability + Ready-made payment systems + API composability
Geographic Strategy* Consider lower cost US regions * Explore international locations with high living standards * Remote work enabling location flexibility
Market Positioning* Consulting opportunities from over-firing * Focus on cost-saving technologies * Build multiple revenue streams * Target sectors needing operational efficiency
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Container Size Optimization in 2025
Core Motivation
-
Container size directly impacts cost efficiency
-
Python containers can reach 5GB
-
Sub-1MB containers enable:
-
Incredible performance
-
Microservice architecture at scale
-
Efficient resource utilization
Container Types Comparison
Scratch (0MB base)
-
Empty filesystem
-
Zero attack surface
-
Ideal for compiled languages
-
Advantages:
-
Fastest deployment
-
Maximum security
-
Explicit dependencies
-
Limitations:
-
Requires static linking
-
No debugging tools
-
Manual configuration required
Example Zig implementation:
```zig
const std = @import("std");
pub fn main() !void {
// Statically linked, zero-allocation server
var server = std.net.StreamServer.init(.{});
defer server.deinit();
try server.listen(try std.net.Address.parseIp("0.0.0.0", 8080));
}
```
Alpine (5MB base)
-
Uses musl libc + busybox
-
Includes APK package manager
-
Advantages:
-
Minimal yet functional
-
Security-focused design
-
Basic debugging capability
-
Limitations:
-
musl compatibility issues
-
Smaller community than Debian
Distroless (10MB base)
-
Google's minimal runtime images
-
Language-specific dependencies
-
No shell/package manager
-
Advantages:
-
Pre-configured runtimes
-
Reduced attack surface
-
Optimized per language
-
Limitations:
-
Limited debugging
-
Language-specific constraints
Debian-slim (60MB base)
-
Stripped Debian with core utilities
-
Includes apt and bash
-
Advantages:
-
Familiar environment
-
Large community
-
Full toolchain
-
Limitations:
-
Larger size
-
Slower deployment
-
Increased attack surface
Modern Language Benefits
Zig Optimizations
```zig
// Minimal binary flags
// -O ReleaseSmall
// -fstrip
// -fsingle-threaded
const std = @import("std");
pub fn main() void {
// Zero runtime overhead
comptime {
@setCold(main);
}
}
```
Key Advantages
-
Static linking capability
-
Fine-grained optimization
-
Zero-allocation options
-
Binary size control
Container Size Strategy
-
Development: Debian-slim
-
Testing: Alpine
-
Production: Distroless/Scratch
-
Target: Sub-1MB containers
Emerging Trends
-
Energy efficiency focus
-
Compiled languages advantage
-
Python limitations exposed:
-
Runtime dependencies
-
No native compilation
-
OS requirements
Implementation Targets
-
Raspberry Pi deployment
-
ARM systems
-
Embedded devices
-
Serverless (AWS Lambda)
-
Container orchestration (K8s, ECS)
Future Outlook
-
Sub-1MB container norm
-
Zig/Rust optimization
-
Security through minimalism
-
Energy-efficient computing
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Regulatory Entrepreneurship and Alternative Governance SystemsKey ConceptsRegulatory Entrepreneurship* Companies building businesses that require changing laws to succeed * Examples: Uber, Airbnb, Tesla, DraftKings, OpenAI * Core strategies: + Operating in legal gray areas + Growing "too big to ban" + Mobilizing users as political force
Comparison with Mafia SystemsCommon Factors
- Emerge when government is ineffective/incompetent
- Provide alternative governance
- Push negative externalities to public
- Promise improvements but often worsen conditions
Key Differences
- VC ecosystem operates in legal gray areas
- Mafia operates in illegal activities
- Tech aims for global scale/influence
Societal ImpactNegative Effects* Increased traffic (Uber) * Housing market disruption (Airbnb) * Financial fraud risks (Crypto/FTX) * Monopolistic tendencies * Democratic erosion
Solutions for GovernmentsDemocracy Strengthening
- Eliminate unlimited lobbying
- Implement wealth taxes
- Provide socialized healthcare/education
- Enable direct democracy through polling
- Develop competent civil service
Technology Independence
- Create public alternatives (social media, AI)
- Support small businesses over monopolies
- Focus on community-based solutions
- Regulate large tech companies
- Protect national sovereignty
Future Implications* Growing tension between tech and traditional governance * Need for balance between innovation and regulation * Importance of maintaining democratic systems * Role of public infrastructure and services
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WebSockets in Rust: From Theory to ImplementationEpisode Notes for Pragmatic Labs Technical Deep Dive
Introduction [00:00-00:45]* WebSockets vs HTTP request-response pattern analogy * Real-time communication model comparison * Rust's zero-cost abstractions and compile-time guarantees * SQLite WebSocket demo introduction
Rust's WebSocket Advantages [01:05-01:47]* Zero-cost abstractions implementation * Memory safety guarantees preventing vulnerabilities * Async/await ecosystem optimization * Strong type system for message handling * Ownership model for connection lifecycles * Cross-platform compilation capabilities
Project Implementation Details [01:53-02:16]* Tokio async runtime efficiency * Structured error handling patterns * Thread-safe SQLite connections * Clean architectural separation * Deployment considerations for embedded systems
WebSocket Core Concepts [02:34-03:35]* Full-duplex TCP communication protocol * Persistent connection characteristics * Bi-directional data flow mechanisms * HTTP upgrade process * Frame-based message transfer * Minimal protocol overhead benefits
Technical Implementation [03:35-04:00]* HTTP request upgrade header process * WebSocket URL scheme structure * Initial handshake protocol * Binary/text message frame handling * Connection management strategies
Advantages Over HTTP [04:00-04:20]* Reduced latency benefits * Lower header overhead * Eliminated connection establishment costs * Server push capabilities * Native browser support * Event-driven architecture suitability
Common Use Cases [04:20-04:36]* Real-time collaboration tools * Live data streaming systems * Financial market data updates * Multiplayer game state synchronization * IoT device communication * Live monitoring systems
Rust Implementation Specifics [04:36-05:16]* Actor model implementation * Connection state management with Arc> * Graceful shutdown with tokio::select * Connection management heartbeats * WebSocket server scaling considerations
Performance Characteristics [05:36-06:15]* Zero-cost futures in practice * Garbage collection elimination * Compile-time guarantee benefits * Predictable memory usage patterns * Reduced server load metrics
Project Structure [06:15-06:52]* ws.rs: Connection handling * db.rs: Database abstraction * errors.rs: Error type hierarchy * models.rs: Data structure definitions * main.rs: System orchestration * Browser API integration points
Real-World Applications [07:10-08:02]* Embedded systems implementation * Computer vision integration * Real-time data processing * Space system applications * Resource-constrained environments
Key Technical Takeaways* Rust's ownership model enables efficient WebSocket implementations * Zero-cost abstractions provide performance benefits * Thread-safety guaranteed through type system * Async runtime optimized for real-time communication * Clean architecture promotes maintainable systems
Resources* Full code examples available on Pragmatic Labs * SQLite WebSocket demo repository * Implementation walkthroughs * Embedded system deployment guides
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Corporate America: A Prison Break GuideKey Themes* Thoreau's "quiet desperation" frames corporate work as voluntary imprisonment * Graeber's 5 BS jobs expose corporate dysfunction: + Flunkies (middle managers) + Goons (HR, enforcement) + Duct-tapers (perpetual problem fixers) + Box-tickers (DEI/compliance) + Taskmasters (productivity enforcers)
Soft Authoritarianism in Corporate Culture* Location control (anti-remote work) * Thought control (shifting ethical stances) * Time control (9-5 structure) * Value suppression (standardized pay bands) * Ethics sacrificed for profit
Resistance Strategy* Minimize meeting attendance * Work remotely when possible * Spend 20% of pay on valuable skill development * Avoid management track * Build uncorrelated income streams: + Consulting + Investments + Side businesses
The Shawshank Strategy* Save 2+ years of living expenses (~$250k buffer) * Develop marketable skills quietly * Create multiple income streams * Reduce expenses/debt * Plan methodical escape
Core MessageCorporate America represents a form of wage slavery, but methodical resistance and skill-building can create paths to freedom and authentic living.
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Zig's Memory Management Philosophy* Explicit and transparent memory management * Runtime error detection vs compile-time checks * No hidden allocations * Must handle allocation errors explicitly using try/defer/ensure * Runtime leak detection capability
Comparison with C and RustC Differences* Safer than C due to explicit memory handling * No "foot guns" or easy-to-create security holes * No forgotten free() calls * Clear memory ownership model
Rust Differences* Rust: Compile-time ownership and borrowing rules + Single owner for memory + Automatic memory freeing + Built-in safety with performance trade-off * Zig: Runtime-focused approach + Explicit allocators passed around + Memory management via defer + No compile-time ownership restrictions + Runtime leak/error checking
Four Types of Zig AllocatorsGeneral Purpose Allocator (GPA)
- Tracks all allocations
- Detects leaks and double-frees
- Like a "librarian tracking books"
- Most commonly used for general programming
Arena Allocator
- Frees all memory at once
- Very fast allocations
- Best for temporary data (e.g., JSON parsing)
- Like "dumping LEGO blocks"
Fixed Buffer Allocator
- Stack memory only, no heap
- Fixed size allocation
- Ideal for embedded systems
- Like a "fixed size box"
Page Allocator
- Direct OS memory access
- Page-aligned blocks
- Best for large applications
- Like "buying land and subdividing"
Real-World Performance ComparisonsBinary Size* Zig "Hello World": ~300KB * Rust "Hello World": ~1.8MB
HTTP Server Sizes* Zig minimal server (Alpine Docker): ~300KB * Rust minimal server (Scratch Docker): ~2MB
Full Stack Example* Zig server with JSON/SQLite: ~850KB * Rust server with JSON/SQLite: ~4.2MB
Runtime Characteristics* Zig: Near-instant startup, ~3KB runtime * Rust: Runtime initialization required, ~100KB runtime size * Zig offers optional runtime overhead * Rust includes mandatory memory safety runtime
The episode concludes by suggesting Zig as a complementary tool alongside Rust, particularly for specialized use cases requiring minimal binary size or runtime overhead, such as embedded systems development.
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AI Propaganda and Market RealityKey Points* LLMs are pattern matching systems, not true AI - similar to established clustering and regression techniques * Innovation follows non-linear path, contrary to VC expectations * VCs require exponential returns - 1/100 investments must generate massive profits * Perfect competition emerging in AI market - open source models reaching parity with commercial ones
Technical Context* LLMs extend existing data science tools: + K-means clustering + Linear regression + Recommendation engines * Pattern matching in multi-dimensional space ≠ intelligence
Market Dynamics* VCs invested expecting exponential growth * Getting logarithmic returns instead * Fear driving two contradictory narratives: + "Use AI or lose job" + "AI will take your jobs"
Historical ParallelSteam engine (1700s) → combustion engine → electric cars (1910-2025)
Demonstrates long adoption curves for transformative tech
RecommendationUse LLMs pragmatically:
- Beneficial for code tasks
- Prefer open source implementations
- Ignore hype from vested interests
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Podcast Episode Notes: Understanding Zig's Place in Modern ProgrammingEpisode OverviewDiscussion of Zig programming language and its positioning among modern compiled languages like Rust and Go.
Key Points Core Value Proposition*
+ Modern compiled language with C/C++-level control
+ Focuses on extreme performance optimization and binary size control
+ Provides granular control without runtime/garbage collection
-
Binary Size Advantages
- Hello World comparison:
- Zig: ~5KB
- Rust: ~300KB
- Web Server comparison:
- Zig: ~80KB
- Rust: ~1.2MB
-
Performance Features
-
Configurable optimization levels
- Optional debug symbols
- Removable thread safety for single-threaded applications
- Predictable memory usage
- C/C++-equivalent or better performance potential
-
Additional Benefits
-
3-10x faster compile times compared to alternatives
- Improved binary startup performance
- Fine-grained control over system resources
- Hello World comparison:
Target Use Cases* Embedded systems * Minimal Docker containers * Systems requiring precise memory control * Performance-critical applications
Positioning* Complementary tool alongside Rust (not a replacement) * Suitable for specific optimization needs (~10-20% of use cases) * Particularly valuable for size-constrained environments
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Wage Slavery: The Modern ChainsOpeningToday we're examining wage slavery through the lens of personal experience and the work of intellectuals like Chomsky and Graeber. We'll explore how modern systems create dependencies that mirror traditional forms of control.
Types of Income (Personal Framework)* Green Money: Passive income (books, investments) * Yellow Money: Consulting work * Red Money: Employment by others + "Taking all the risk, they get all the upside"
Systemic Controls1. Immigration Status* H-1B visa dependency * Residency tied to employment * Personal example: "I once had a boss threaten to deport me"
- Healthcare Bondage* Survival tied to employment
- "Stay or die" choice
-
Medical access as corporate leverage
-
Student Debt Trap* Non-dischargeable since late 70s
- Forced degree requirements
- Manufactured moral obligation
-
"Did you even have a choice?"
-
Government Capture* Citizens United impact
- Corporate donation influence
- Systematic worker rights erosion
Chomsky's Freedom Framework* Work Control: What, when, where * Time Autonomy: Schedules, breaks, "even bathroom visits" * Belief Systems: Corporate culture compliance * "Even a dog has more control over bathroom breaks"
Graeber's AnalysisBullshit Jobs Categories* Flunkies: Status enhancers * Goons: Aggressive roles * Duct Tapers: Preventable problem fixers * Box Tickers: Work illusionists * Taskmasters: Unnecessary oversight
Debt as Control* Predates money * Corporate vs personal bankruptcy double standard * Modern chains: student, consumer, housing debt * "Moral obligation engineered"
Closing Thoughts* Question why: Schedule, location, tasks * Escape strategies + Geographic arbitrage + Debt avoidance + Healthcare alternatives * "Choose what to do with your life, don't let others choose for you"
Key Quote"Modern slavery doesn't use physical chains, but the control mechanisms are very similar."
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Programming Language Evolution: Data-Driven Analysis of Future TrendsEpisode OverviewAnalysis of programming language rankings through the lens of modern requirements, adjusting popularity metrics with quantitative factors including safety features, energy efficiency, and temporal relevance.
Key Segments1. Traditional Rankings Limitations (00:00-01:53)* TIOBE Index raw rankings examined * Python dominance (23.88% market share) analyzed * Discussion of interpretted language limitations * Historical context of legacy languages * C++ performance characteristics vs safety trade-offs
-
Current Market Leaders Analysis (01:53-04:21)* Detailed breakdown of top languages:
- Python (23.88%): Interpretted, dynamic typing
- C++ (11.37%): Performance focused
- Java (10.66%): JVM-based
- C (9.84%): Systems level
- C# (4.12%): Microsoft ecosystem
- JavaScript (3.78%): Web-focused
- SQL (2.87%): Domain-specific
- Go (2.26%): Modern compiled
- Delphi (2.18%): Object Pascal
- Visual Basic (2.04%): Legacy managed
-
Modern Requirements Deep Dive (04:21-06:32)* Energy efficiency considerations
- Memory safety paradigms
- Concurrency support analysis
- Package management evolution
-
Modern compilation techniques
-
Future-Oriented Rankings (06:32-08:38)1. Rust
- Memory safety without GC
- Ownership/borrowing system
- Advanced concurrency primitives
- Cargo package management
-
Go
-
Cloud infrastructure optimization
- Goroutine-based concurrency
- Simplified systems programming
- Energy efficient garbage collection
-
Zig
-
Manual memory management
- Compile-time features
- Systems/embedded focus
- Modern C alternative
-
Swift
-
ARC memory management
- Strong type system
- Modern language features
- Performance optimization
-
Carbon/Mojo
-
Experimental successors
- Modern safety features
- Performance characteristics
- Next-generation compilation
-
Future Predictions (08:38-10:51)* Shift away from legacy languages
- Focus on energy efficiency
- Safety-first design principles
- Compilation vs interpretation
- AI/ML impact on language design
Key Insights1. Language Evolution Metrics
* Safety features
* Energy efficiency
* Modern compilation techniques
* Package management
* Concurrency support
-
Legacy Language Challenges
- Technical debt
- Performance limitations
- Safety compromises
- Energy inefficiency
- Package management complexity
-
Future-Focused Features
-
Memory safety guarantees
- Concurrent computation
- Energy optimization
- Modern tooling integration
- AI/ML compatibility
Production NotesTarget Audience* Professional developers * Technical architects * System designers * Software engineering students
Key Timestamps* 00:54 - TIOBE Index introduction * 04:21 - Modern language requirements * 06:32 - Future-oriented rankings * 08:38 - Predictions and analysis * 10:34 - Concluding insights
Follow-up Episode Topics1. Deep dive into Rust vs Go trade-offs 2. Energy efficiency benchmarking 3. Memory safety paradigms comparison 4. Modern compilation techniques 5. AI/ML impact on language design
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Corporate America & VC Startup Scams: System-Level AnalysisEpisode OverviewCritical analysis of systemic failures in corporate America and VC-funded startups. Focus on structural exploitation, control mechanisms, and loss of autonomy.
Corporate America: Core System Failures1. Ultra-Capitalist Firing Culture* At-will employment enables arbitrary termination * Performance metrics deliberately shift to justify cuts * Stack ranking creates artificial scarcity, forces competition
- High Salary Lock-in Trap* $500K salary = $10K/month Bay Area mortgage
- Geographic trap via compensation
-
Monopoly power enhanced through location-based pay
-
CEO Compensation Asymmetry* 1400-5000x worker pay ratio
- RSU/stock option disparity masks true gap
-
Executive incentives tied to worker exploitation
-
Ethical Compromise Framework* Mortgage pressure forces compliance
- Technical debt accumulation from rushed delivery
-
Privacy/security concerns ignored for quarterly targets
-
Post-1980 Rights Erosion* Pension elimination: Fixed benefit → market risk
- Healthcare as control mechanism
-
Stagnant wages despite productivity gains
-
Autonomy Elimination* On-call rotations control personal time
- Multi-layer approval chains
-
Career paths dictated by org needs
-
Skills Extraction Pipeline* One-way knowledge transfer
- IP rights stripped via documentation
-
Forced training of replacements
-
Location Control* Remote work tied to metrics
- Artificial office mandates
- COL adjustments as punishment
VC Startup Structural Issues1. Philosophical Misalignment* Libertarian/anarchist VC ecosystem * Growth over sustainability * Exit priority over product quality
- Asymmetric Risk* 100-hour founder/employee weeks
- VCs spread risk across 100+ companies
-
Burnout as feature, not bug
-
Control Transfer* Board supersedes founder vision
- Hidden term sheet provisions
-
Preferred stock structure traps
-
Wealth Concentration Mechanisms* Cap table waterfall favors VCs
- Common stock dilution
-
Underwater options post-down round
-
False Entrepreneurship* Founders become middle managers
- Innovation constrained by VCs
-
Product roadmap dictated by TAM
-
Burn Rate Trap* Growth metrics require constant fundraising
- Tech hub talent cost spikes
-
Infrastructure over-provisioning
-
Single Point Dependencies* One bad quarter kills funding
- Market timing dictates survival
- Competitor rounds force exits
Alternative System DesignBootstrap Path* Consulting-based revenue (yellow money) * Build passive income streams * Maintain low burn rate * Geographic arbitrage * True autonomy preservation
Key Metrics for Success* Wake-up freedom * Work selection control * Ethics alignment * Healthcare independence * Retirement capability * Location flexibility
Core ThesisTrue innovation and freedom require breaking from traditional corporate/VC systems. Focus on autonomy preservation through bootstrap methodology.
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Systems Engineering: Rust vs Python AnalysisCore Principle: Delete What You KnowTechnology requires constant reassessment. Six-month deprecation cycle for skills/tools.
Memory Safety Architecture* Compile-time memory validation * Zero-cost abstractions eliminate GC overhead * Production metrics: 30% CPU reduction vs Python services
Performance Characteristics* Default performance matters (electric car vs 1968 Suburban analogy) * No GIL bottleneck = true parallelism * Direct hardware access capability * Deterministic operation timing
Concurrency Engineering* Type system prevents race conditions by design * Real parallel processing vs Python's IO-bound concurrency * Async/await with actual hardware utilization
Type System Benefits* Compilation = runtime validation * No 3AM TypeError incidents * Superior to Python's bolt-on typing (Pydantic) * IDE integration for systems development
Package Management Infrastructure* Cargo: deterministic dependency resolution * Single source of truth vs Python's fragmented ecosystem (venv/conda/poetry) * Eliminates "works on my machine" syndrome
Systems Programming Capabilities* Zero-overhead FFI * Embedded systems support * Kernel module development potential
Production Architecture* Native cross-compilation (x86/ARM) * Minimal runtime footprint * Docker images: 10MB vs Python's 200MB
Engineering Productivity* Built-in tooling (rustfmt, clippy) * First-class documentation * IDE support for systems development
Cloud-Native Development* AWS Lambda core uses Rust * Cost optimization through CPU/memory efficiency * Growing ML/LLM ecosystem
Systems Design Philosophy* "Wash the Cup" principle: Build once, maintain forever * Compiler-driven refactoring * Technical debt caught at compile-time * 80% reduction in runtime issues
Deployment Architecture* Single binary deployment * Cross-compilation support * ECR storage reduction: 95% * Elimination of dependency hell
Python's Appropriate Use Cases* Standard library utilities * Quick scripts without dependencies * Notebook experimentation * Not suited for production-scale systems
Key InsightProduction systems demand predictable performance, memory safety, and deployment certainty. Rust delivers these by design.
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UN Digital Human Rights Extensions: Key PointsArticle 3: Right to Life, Liberty, Security* Protection from digitally-coordinated violence and mob incitement * Safeguards against viral misinformation causing physical harm * Emergency protocols for platform-amplified unrest
Article 17: Property Rights* Prevent monopolistic control of digital property * Mandate platform interoperability * Protect data ownership and creative works * Combat trillion-dollar companies' unauthorized use of content
Article 19: Freedom of Expression* Protection against coordinated disinformation * Transparent content moderation requirements * Preservation of independent journalism * Combat algorithmic suppression of truth
Article 20: Freedom of Assembly* Distinguish between organic vs artificially incited assemblies * Platform liability for amplifying dangerous falsehoods * Rapid content moderation during civil unrest
Article 21: Democratic Participation* Prevent digital election interference * Require transparent political advertising * Protect against algorithmic manipulation * Address unlimited corporate political spending
Article 23: Work Rights* Protection against predatory gig economy practices * Fair marketplace access * Defense of local businesses against monopolies * Support for union organization
Article 28: Social Order* Restrict tech lobbying influence * Require transparency in political contributions * Prevent digital gerrymandering * Protect democracy from corporate control
Key Concerns* US tech companies violating human rights globally * Need for UN oversight and enforcement * Focus on platform accountability * Protection of democratic processes
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Food Industry Self-Regulation: A Case Study in Regulatory EconomicsKey Statistical Evidence Self-Regulation Metrics (2000-Present)* + 98.7% of food additives introduced through self-regulation + 756 novel ingredients added without rigorous safety evidence + Demonstrates significant Type II error risk in regulatory framework
Regulatory Framework ComparisonUnited States ModelCurrent Regulatory Architecture
- Predominantly voluntary compliance mechanisms
- Post-market surveillance limitations
- Harvard analysis (Broad-Leib) indicates systemic regulatory capture
Case Study: Trans Fats
- Temporal lag between identification of health risks (1950s) and regulatory action
- Demonstrates β-error in regulatory hypothesis testing
- Significant public health externalities observed
European Union ModelPrecautionary Principle Framework
- Ex ante regulatory approach
- Centralized database implementation
- Proactive additive review methodology
Empirical Outcomes
- Observable differences in food composition
- Lower processed ingredient density
- Correlation with improved public health metrics
- Lower obesity rates and higher life expectancy (causality implied but not proven)
Economic ImplicationsMarket FailuresInformation Asymmetry
- Consumers lack complete ingredient transparency
- Principal-agent problem in food safety
- Market efficiency degradation
Negative Externalities
- Public health costs
- Disproportionate impact on lower socioeconomic strata
- Systemic healthcare burden
Parallel to Technology SectorRegulatory Pattern AnalysisSimilar Arguments Against Regulation
- Innovation impediment claims
- Market efficiency arguments
- Self-regulation advocacy
Key Differences
- Information goods vs. physical goods
- Network effects considerations
- Systemic risk profiles
Theoretical FrameworkRegulatory EconomicsOptimal Regulation Theory
- Balance between market freedom and consumer protection
- Cost-benefit analysis of regulatory intervention
- Dynamic efficiency considerations
Public Choice Implications
- Concentrated benefits, diffuse costs
- Regulatory capture mechanisms
- Interest group dynamics
Conclusions* Empirical evidence supports stronger regulatory frameworks * Self-regulation demonstrates significant market failures * Parallel patterns emerging in technology sector regulation * Public health and democratic implications require consideration
This analysis suggests that the food industry case study provides valuable insights into the limitations of self-regulation in markets with significant information asymmetries and externalities.
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Episode Notes: Europe vs America - Regulations and InnovationCore ArgumentThe common meme "Europe makes laws, America makes products" represents an oversimplified view of complex regulatory and innovation dynamics between the regions.
Organizational RealitiesBureaucratic Challenges* Inefficient positions in universities and corporations * VP roles that provide minimal value * Team productivity issues (tasks taking 1 year vs 1 day) * Parkinson's Law impact: Work expanding to fill available time * Political maneuvering in corporate hierarchies
Regulatory PurposeExamples from "Alone Australia":
- Protection of endangered species
- Preservation of natural resources
- Environmental sustainability
- Prevention of exploitation
Economic and Social AnalysisVenture Capital Critique* Short-term value extraction vs long-term sustainability * Impact of unregulated market approaches * Consequences of prioritizing immediate profits * Need for balanced economic development
American System Challenges1. Healthcare Issues
* Primary cause of bankruptcy
* Comparison with other developed nations
* Impact on middle and lower-income populations
-
Public Health Metrics
- Life expectancy comparisons
- Healthcare system efficiency
- Population health outcomes
-
Safety and Security
-
Gun violence statistics
- Child safety concerns
- Regulatory gaps
-
Economic Disparity
-
Historical income inequality trends
- Electoral system influences
- Corporate power concentration
European ConsiderationsSuccessful Systems to Maintain* Universal healthcare access * Efficient public transportation * Higher life expectancy * Quality of life priorities
Innovation Recommendations* Support for small team structures * Competition enhancement * Anti-monopolistic policies * Sustainable development focus
Data Science PerspectiveBased on experience from:
- UC Berkeley
- Duke University
- Northwestern University
- UC Davis
- Corporate and startup environments
Measurement Metrics* Population health indicators * Economic stability factors * Social welfare measures * Environmental sustainability * Innovation outputs
Key Insights1. Regulation serves essential protective functions 2. Uncontrolled deregulation can lead to systemic problems 3. Balance between innovation and protection is achievable 4. Small team efficiency can coexist with regulatory frameworks 5. Economic metrics should include social and environmental factors
ConclusionThe path forward involves maintaining effective regulations while fostering innovation through controlled competition and sustainable development practices. Europe can learn from both American successes and failures while preserving its own effective systems.
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🎯 Breaking Down "Gaslighting Your Way to Responsible AI" - A Critical Analysis of Tech Ethics
Here are the key insights from this thought-provoking discussion on AI ethics and corporate responsibility:
Meta's Ethical ConcernsCourt documents revealed Meta allegedly used 82 terabytes of pirated books for AI training, with leadership awareness of ethical breaches
CEO Mark Zuckerberg reportedly encouraged moving forward despite known ethical concerns
Internal communications showed employee discomfort with using corporate resources for potentially illegal activities
The Gaslighting PlaybookLarge tech companies often frame conversations around "responsible AI" while engaging in questionable practices
Pattern mirrors historical examples from food and tobacco industries:
Food industry deflecting sugar's health impacts
Tobacco companies leveraging physician endorsements despite known cancer risks
Corporate Influence TacticsHeavy investment in:
Elite university partnerships
Congressional lobbying
Nonprofit organization donations (Python Software Foundation, Linux Foundation)
Goal: Legitimizing practices through institutional credibility
Monopoly Power ConcernsMeta's acquisition strategy (Instagram, WhatsApp) highlighted as example of reduced competition
Centralization of power enabling further influence through:
Political donations
Academic partnerships
Nonprofit funding
Technology Capability ClaimsCurrent AI capabilities often overstated
Large language models described as "fancy search engines" rather than truly intelligent systems
Full self-driving claims questioned given current technological limitations
Path Forward RecommendationsNeed for independent trust institutions
Critical thinking and questioning of corporate narratives
Sensible government regulation without hindering innovation
European regulatory approach cited as potential model
🔥 Ready to dive deeper into responsible AI development and ethical tech practices? Join our community at https://ds500.paiml.com/subscribe.html for exclusive insights and practical guidance on building AI systems that truly serve humanity. #ResponsibleAI #TechEthics #AIGrowth #DigitalEthics #TechLeadership
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🚀 Pragmatic AI Labs - Interactive Rust Labs Launch AnnouncementKey AnnouncementsPragmatic AI Labs has launched browser-based interactive Rust labs, removing traditional setup barriers and providing an instant-access development environment through Visual Studio Code in the browser
The platform offers a comprehensive learning experience with pre-configured Rust environments, eliminating the need for manual installation or setup
Future roadmap includes the upcoming release of GPU-based labs, demonstrating the platform's commitment to advanced technical education
Platform FeaturesFull Visual Studio Code browser environment
Pre-configured Rust development setup
Comprehensive example codebase with detailed documentation
Integrated terminal access for direct compilation
Browser-based access at ds500.pa.ml
Educational Value PropositionPlatform hosts equivalent of 3+ master's degrees worth of educational content
Focus on democratizing technical education
Hands-on, practical learning approach with interactive coding environments
What's NextGPU-based labs in development
Continued expansion of educational content
Enhanced learning resources and documentation
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2030: The Silent Tech Invasion of EuropeCore Premise Scenario: Elon Musk systematically dismantles European governance * Method: Algorithmic conquest via social media * Year: 2030 * Targets*: Germany, UK, France, Italy, Spain
Key Systemic Vulnerabilities* Unchecked corporate influence in politics * Exponential income inequality * Lack of tech regulation
American Anti-Patterns Europe Must AvoidMonopoly Culture
- Tech oligarchies suppressing innovation
- Examples: Microsoft, Meta acquisitions
- Preventing genuine small business innovation
Venture Capital Problematic Trends
- Creating rent-seeking products
- Destructive "innovations" like:
- Uber (destroys unions, increases traffic)
- Airbnb (causes housing crises)
Democratic Erosion
- Unlimited corporate political donations
- Unelected tech leaders influencing governance
Recommended European Defensive Strategies* Implement massive wealth tax * Strengthen tech regulation * Prevent monopolistic tech acquisitions * Protect democratic processes
WarningUnless corrective actions are taken, Europe risks a "silent invasion" by tech oligarchs by 2030
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Here are the episode notes:
How EU/Commonwealth Can Protect Democracy from Big TechKey Defensive MeasuresWealth Control Mechanisms* Treat extreme wealth ($100B+) like hostile nation states * Implement tariffs against ultra-wealthy individuals * Adopt progressive wealth taxation (Spanish model) * Cap individual wealth accumulation
Social Media Regulation* Tax platforms based on misinformation volume (e.g., 80% misinfo = 80% profit tax) * Consider under-18 social media restrictions * Address degradation of local journalism/business * Recognize parallels to historical propaganda (French Revolution pamphlets)
Tech Sovereignty Protection* Adopt open source over proprietary systems + Linux vs Windows example + 90% global infrastructure runs on Linux + Open source dominates top 25 programming languages + Most established databases are open source * Resist Bay Area VC/Tech influence * Regulate gig economy "slave wear" platforms * Control local service operations
Proactive Defense Strategy* Implement aggressive wealth taxation * Apply targeted tech company tariffs * Mandate open source in government systems * Regulate misinformation vectors * Protect national digital sovereignty
Summary:
A systems analysis of how EU/Commonwealth nations can defend against tech oligarchy influence. Core recommendation is treating extreme wealth/tech concentration as national security threat. Advises aggressive regulation via taxation, open source adoption, and sovereignty protection measures. Keys: wealth caps, misinfo taxes, open source transition, local control of services. Notes parallel between social media and historical propaganda systems.
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Episode Notes: AI Industry Transitions and Workforce ProposalsOverviewA technical analysis of proposed career transitions for OpenAI engineers, presented through the lens of market dynamics and workforce displacement patterns.
Key Timestamps and Analysis[00:00:00] - Context and Premise* Initial framing of workforce transition proposals * Reference to Sam Altman's 2024 UBI commentary * Juxtaposition of AI displacement predictions with internal corporate dynamics
[00:00:27] - Data Rights and Attribution Analysis* Discussion of intellectual property attribution challenges * Examination of content scraping methodologies * Critical analysis of training data sourcing practices
[00:01:31] - Market Dynamics* Comparative analysis of model pricing ($200 licensing fee) * Market disruption by DeepSeek's zero-cost alternative implementation * Impact on service valuation and market positioning
[00:01:48] - Proposed Transition VectorsTechnical to Trade Transitions
- Plumbing sector analysis
- Market demand evaluation
- Skill transferability assessment
- Infrastructure maintenance parallels
Leadership Transitions
- Analysis of public-facing roles
- Market positioning strategies
- Revenue model adaptations
Data Operations
- Chinese AI ecosystem integration
- Data labeling specialization
- Cross-market skill application
[00:03:46] - Creative Sector Integration* Apprenticeship models in visual arts * Skill transfer mechanisms * Market reentry pathways
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Core Strengths of DeepSeek's Approach1. Open Source Innovation * Slashed API costs to 1/30th of OpenAI's * Focuses on affordability and accessibility * Triggered price competition with ByteDance and Ali Cloud 1. Original Research Philosophy * Prioritizes foundational research over quick commercialization * Developed MLA architecture as transformer alternative * Aims to lead through new designs rather than imitation 1. Long-term Research Focus * Commits to fundamental breakthroughs over quick profits * Not constrained by existing revenue streams * Emphasizes patient capital for major innovations 1. Strategic Specialization * Focuses solely on core model research * Avoids diversification into apps/products * Enables deeper expertise in foundational AI
US Tech Industry Challenges1. Regulatory and Market Issues * Big Tech focuses on regulatory capture * Lobbying for AI safety rules favoring incumbents * Emphasis on closed ecosystems over innovation 1. Innovation Barriers * Large companies prioritize incremental updates * Focus on vertical integration through acquisitions * Risk-averse R&D approach 1. Structural Problems * Short-term profit focus * Talent concentration in big tech * Healthcare/education costs limiting entrepreneurship * Income inequality affecting innovation pipeline 1. Cultural Factors * Elite clustering in top tech roles * Resource barriers to STEM education * Focus on pedigree over merit * Transactional versus collaborative culture
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DeepSeek R1 and Open Source AI: A Case for Open SolutionsKey PointsUnderstanding "Downloading" in Context* Clarifies misconceptions about downloading software * Distinguishes between smartphone apps and open-source solutions * Uses Linux as an example of successful open-source software + Speaker uses Ubuntu personally + Other variants mentioned: Kubuntu, Mint, Pop OS
Benefits of Open Solutions* Allows code inspection and transparency * Free to use and modify * Community can contribute bug fixes and features * Contrasts with closed systems like Windows and macOS * Ability to verify data isn't being transmitted externally
How to Access DeepSeek R1* Available through ollama.com/library/deepseekr1 * Installation methods: + GUI interfaces available + Command line usage: ollama run deep-seek-r1 * Alternative platforms mentioned: + Llamafile + Hugging Face Candle (Rust-based solution)
Data Privacy and Ethics* Emphasis on ethical data sourcing + Consensual data collection + Examples: Wikipedia with explicit terms of service * Criticism of regional bias in tech evaluation + Arguments against "China vs USA" comparisons + Focus should be on regulatory frameworks + Praises EU's data privacy regulations
Criticism of Closed Systems* Windows OS cited as example of problematic closed system + Historical monopolistic practices + Current privacy concerns with data collection * Critique of venture capital's role in tech + Examples: Uber (worker protection issues) + Airbnb (housing market impacts) * Concerns about corporate control of mathematical tools
Call to Action* Encourage adoption of open models * Get involved in open-source AI communities * Advocate for open solutions in workplace * Be skeptical of fear, uncertainty, and doubt (FUD) tactics * Avoid closed solutions like GitHub Copilot, Microsoft products, or OpenAI services
Historical Context* References "Halloween Documents" leak exposing Microsoft's anti-Linux strategy * Discusses Bill Gates's historical opposition to open-source software * Points to success of open-source programming languages and Linux in server market
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NVIDIA's AI Empire: A Hidden Systemic Risk?Episode OverviewA deep dive into the potential vulnerabilities in NVIDIA's AI-driven business model and what it means for the future of AI computing.
Key PointsThe Current State* NVIDIA generates 80-85% of revenue from AI workloads (2024) * Data Center segment alone: $22.6B in a single quarter * Heavily concentrated business model in AI computing
The China Scenario* Potential development of alternative AI computing solutions * Historical precedents exist: + Google's TPU (TensorFlow Processing Unit) + Amazon's FPGAs + Custom deep learning chips
The Three Phases of DisruptionInitial Questions
- Unusual patterns in Chinese AI development
- Cost anomalies despite chip restrictions
- Market speculation begins
Market Realization
- Chinese firms demonstrate alternative solutions
- Western companies notice performance metrics
- Questions about GPU necessity arise
Global Cascade
- Western tech giants reassess GPU dependence
- Alternative solutions gain credibility
- Potential rapid shift in AI infrastructure
Comparative Business Risk* Unlike diversified tech giants (Apple, Microsoft, Amazon, Google): + NVIDIA's concentration in one sector creates vulnerability + 80%+ revenue from single source (AI workloads) + Limited fallback options if AI computing paradigm shifts
Historical Context* Reference to TPU development by Google * Amazon's work with FPGAs * Evolution of custom AI chips
Broader Industry Implications* Impact on AI training costs * Potential democratization of AI infrastructure * Shift in compute paradigms
Discussion Points for Listeners* Is concentration in AI computing a broader industry risk? * How might this affect the future of AI development? * What are the parallels with other tech disruptions?
Key Closing ThoughtThe real systemic risk isn't just about NVIDIA - it's about betting the future of AI on a single computational approach. Even if the probability is low, the impact could be devastating given the concentration of risk.
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The AI Race and Open Source Development: Episode NotesMain Discussion PointsHistorical Comparison Analysis* Discussion of a VC's comparison between current AI developments and the 1957 Sputnik moment * Examination of historical context: + 1950s tax structure (91% individual rate, 52% corporate) + Government funding mechanisms + Public sector innovation patterns
Open Source Software Development* Evolution of open source software since 1991 * Notable open source milestones: + Linux operating system + Python programming language + Apache web server * Discussion of open source characteristics: + Peer review processes + Community-driven development + Security validation methods
Technology Industry Analysis* Examination of venture capital investment patterns * Case study of ride-sharing technology: + Impact on urban transportation + Economic model comparison + Infrastructure utilization
AI Development Landscape* Current state of AI model development * Comparison of closed versus open source approaches * Role of academic institutions in AI research * Discussion of model replication and validation
Regulatory and Ethical Considerations* Dataset transparency discussion * Content ownership considerations * Ethical oversight mechanisms * International collaboration frameworks
Technical Details* Discussion of model architectures * Development methodology comparisons * Resource allocation patterns * Implementation strategies
Concluding Points* Analysis of global versus national development approaches * Future predictions for AI development patterns * Discussion of collaborative development models
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Podcast Episode Notes: The Fate of Closed LLMs and the Legacy of Proprietary Unix SystemsSummaryThe episode draws parallels between the decline of proprietary Unix systems (Solaris, SGI) and the potential challenges facing closed-source large language models (LLMs) like OpenAI. The discussion highlights historical examples of corporate stagnation, the rise of open-source alternatives, and the risks of vendor lock-in. Key themes include innovation dynamics, community-driven development, and predictions for the future of AI.
Key Topics Discussed1. Historical Precedent: The Fall of Solaris and SGI* Proprietary Unix systems (Solaris, SGI) dominated IT infrastructure in the 2000s but declined due to: + Corporate mergers (e.g., Oracle’s acquisition of Sun) stifling innovation. + High costs vs. affordable, open-source Linux alternatives. * Example: Caltech’s expensive SGI/Solaris systems were replaced by cheaper Linux machines.
- Parallels to Modern LLMs OpenAI’s trajectory*:
- Initial innovation, but risks of stagnation under corporate partnerships (e.g., Microsoft).
- Potential for “hippocratic” decision-making (highest-paid person’s opinion) over user needs.
-
Market dynamics:
- Open-source LLMs (e.g., DeepSeek) are gaining parity or surpassing closed systems.
- Commoditization of AI tools mirrors the shift from Unix to Linux.
-
Challenges of Closed Systems Vendor lock-in*: Aggressive pricing and opaque practices (e.g., Oracle, Microsoft).
- Trust issues: Data privacy concerns with proprietary systems vs. local, open alternatives.
-
Innovation lag: Closed systems lack community input, leading to features users don’t want.
-
The Open-Source Advantage* Community-driven development often outperforms proprietary solutions (e.g., LibreOffice vs. Microsoft Office).
-
Global momentum: Regions like Europe, China, and India may adopt open-source LLMs to avoid dependency on U.S. tech giants.
-
Future Predictions “Sudden death” of closed LLMs*: Similar to proprietary Unix, closed AI systems may collapse under high costs and low ROI.
- Rise of small, specialized models: Democratization of AI through open frameworks.
- Hype vs. reality: Corporate claims about AGI and AI capabilities should be met with skepticism (e.g., “divide by 10”).
Notable Quotes On innovation:
“Open source starts to exceed the user experience of closed source because you don’t have a community developing something.”
* On corporate practices:
“Billionaires running corporations lie big because they want you to believe what they’re doing.”
* On trust:
“In a closed system, your data goes to some proprietary system you don’t trust. In an open system, you do those queries locally.”*
ConclusionThe episode argues that closed LLMs like OpenAI risk following the path of Solaris and SGI: initial dominance followed by decline as open-source alternatives outpace them in innovation, cost, and trust. The future of AI may lie in decentralized, community-driven models, challenging the narrative that closed systems are the only way forward. Skepticism toward corporate hype and advocacy for open frameworks are key takeaways. 🌍🔓
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Podcast Episode Notes: Red Flags in Tech Fraud – Historical Cases & OpenAISummaryThis episode explores common red flags in high-profile tech fraud cases (Theranos, FTX, Enron) and examines whether similar patterns could apply to OpenAI. While no fraud is proven, these observations highlight risks worth scrutinizing.
Key Red Flags & Historical Parallels🚩 Unverifiable Claims Theranos: Elizabeth Holmes’ claims about “one drop of blood” diagnostics were never independently validated. * OpenAI*: Claims about AGI (Artificial General Intelligence) being “imminent” lack third-party verification. Critics argue OpenAI redefined AGI as “$100B in profit,” a misleading pivot.
“AGI and $100B in profit… those two words don’t have any relation to each other.”
🚩 Test Manipulation Theranos: Faked blood test results using external labs while claiming proprietary tech. * OpenAI: Questions about benchmarks like Frontier Math*, a nonprofit funded by OpenAI. Is performance data being gamed without independent oversight?
🚩 Employee Exits & Whistleblower Cases FTX/Theranos/Enron: Mass exits and whistleblowers preceded collapses. * OpenAI*: High-profile safety researchers have departed. An open whistleblower case involves an unexplained death (under investigation).
🚩 IP Theft Lawsuits Theranos: Faced lawsuits over stolen intellectual property. * OpenAI*: NY Times lawsuit alleges unauthorized use of copyrighted training data. Scrutiny grows over data sourcing practices.
🚩 Structural Changes FTX/WeWork: Opaque corporate restructuring masked risks. * OpenAI*: Shift from nonprofit to for-profit (capped-profit LP) raises questions. How does Microsoft’s stake impact governance and transparency?
🚩 Whistleblower Suppression Theranos: Whistleblowers faced legal threats and familial pressure. * OpenAI*: NDAs and legal actions reportedly silence critics. A deceased whistleblower’s case remains unresolved.
🚩 Excess Secrecy Enron/FTX: Hidden financial schemes and tech failures. * OpenAI*: Core AI models are proprietary, yet open-source rivals (e.g., Chinese firms) claim comparable results with minimal funding.
“A random Chinese company… built something better for $5M. Is OpenAI worth $157B?”
🚩 Regulatory Evasion Theranos/FTX: Avoided FDA/SEC oversight via loopholes. * OpenAI*: Lobbies governments to shape AI regulations, potentially avoiding stricter rules.
🚩 Valuation Concerns FTX: Collapsed after $32B valuation proved inflated. * OpenAI*: $157B valuation clashes with low-cost competitors. Could replication by smaller players destabilize its market position?
Closing ThoughtsWhile OpenAI’s innovations are groundbreaking, historical precedents remind us to critically assess:
- Lack of independent verification
- Opaque governance
- Rapid valuation growth amid legal/ethical risks
Caution: These are observational parallels, not accusations. Time will reveal whether these red flags signify smoke—or just noise.
Further Reading/References* Theranos Fraud Case (SEC) * NY Times vs. OpenAI Lawsuit * TechCrunch: “OpenAI’s Frontier Math & Nonprofit Ties” (2023) * “Bad Blood” (Theranos) by John Carreyrou
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Podcast Notes & Summary: "Deep-Seek Exposes America's Monopoly Problem"Key Topics Discussed Monopolies in Big Tech * Startup Ecosystem Challenges * Regulatory Entrepreneurship * Healthcare & Innovation Barriers * Global Tech Leadership Shifts*
Detailed Notes with Timestamps00:00:00 - 00:00:50 | Introduction to America's Monopoly Problem Issue: Chinese companies outcompeting U.S. tech giants despite America's perceived dominance. * Root Causes*: + Monopolies stifling innovation (e.g., Microsoft vs. Linux). + Tech oligarchs influencing government policies. + "Fear, uncertainty, doubt" (FUD) tactics by monopolies to suppress competition.
00:00:50 - 00:04:00 | Big Tech’s Anti-Competitive Practices Microsoft & Linux: Halloween Docs leak revealed misinformation campaigns against Linux. * Meta’s Acquisitions: Buying competitors like Instagram/WhatsApp to eliminate threats. * Google’s Decline: Market dominance leading to inferior search quality vs. alternatives like Kagi. * Talent Drain*: High salaries at monopolies centralize talent, reducing innovation elsewhere.
00:04:00 - 00:07:00 | Startups: Innovation or Exploitation? Startup Reality: Focus on "explosive exits" over sustainable innovation. * Example: Uber’s $80 ride vs. affordable, efficient public transit. * Regulatory Entrepreneurship*: Startups exploit legal gray areas (e.g., Airbnb’s impact on housing).
00:07:00 - 00:11:00 | OpenAI & Y Combinator’s Role OpenAI’s Controversy: Use of potentially pirated datasets and regulatory gray areas. * Y Combinator’s Model*: High-risk startups funded for outsized exits, ignoring externalities.
00:11:00 - 00:16:00 | Systemic Barriers to Innovation Healthcare System: High costs and bankruptcy risks deter entrepreneurs. * Income Inequality: CEO pay vs. worker wages incentivizes short-term profits over innovation. * Education*: Universities funneling students into incubators, creating dependency.
00:16:00 - 00:16:44 | Global Leadership Shift Europe’s Potential: + Balanced regulations (e.g., GDPR). + Affordable healthcare and quality of life. + Reduced bureaucracy could foster tech leadership. * America’s Decline*: Post-1980s focus on "fake innovation" and exploitative practices.
SummaryKey ArgumentsMonopolies Underperform:
- Big tech (Microsoft, Meta, Google) uses anti-competitive tactics, not innovation, to dominate.
- Talent centralization and excessive CEO pay harm long-term progress.
Startups ≠ Innovation:
- Many prioritize risky exits (e.g., Uber, Airbnb) over solving real problems.
- "Regulatory entrepreneurship" externalizes costs (e.g., housing crises, data piracy).
Healthcare & Inequality:
- U.S. healthcare costs and income inequality deter risk-taking by entrepreneurs.
- Startups rely on incubators, creating pseudo-entrepreneurs dependent on venture capital.
Europe’s Opportunity:
- Balanced regulations, healthcare, and quality of life could position Europe as a tech leader.
- Learning from U.S./China mistakes to prioritize societal benefits over corporate profits.
Conclusion* The U.S. tech dominance narrative is flawed due to systemic issues (monopolies, healthcare, inequality). * Future innovation leadership may shift to regions like Europe or Asia that address these systemic gaps holistically.
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Dual Model Context Code Review: A New AI Development WorkflowIntroductionA novel AI-assisted development workflow called dual model context code review challenges traditional approaches like GitHub Copilot by focusing on building initial scaffolding before leveraging AI with comprehensive context.
Context-Driven Development ProcessIn Rust development, the workflow begins with structured prompts that specify requirements such as file size limits (50 lines) and basic project structure using main.rs and lib.rs. After creating the initial prototype, developers feed the entire project context—including source files, readme, and tests—into AI tools like Claude or AWS Bedrock with Anthropic Sonnet. This comprehensive approach enables targeted requests for features, tests, documentation improvements, and CLI enhancements.
Single Model LimitationsWhile context-driven development proves effective, single-model approaches face inherent constraints. For example, Claude consistently struggles with regular expressions despite its overall 95% effectiveness rate. These systematic failures require strategic mitigation approaches.
Implementing the Dual Model ApproachThe solution involves leveraging DeepSeek as a secondary code review tool. After receiving initial suggestions from Claude, developers can run local code reviews using DeepSeek through Ollama or DeepSeek chat. This additional layer of review helps identify potential critical failures and provides complementary perspectives on code quality.
Distributed AI Development StrategyThis approach mirrors distributed computing principles by acknowledging inevitable failure points in individual models. Multiple model usage helps circumvent limitations like bias or censorship that might affect single models. Through redundancy and multiple perspectives, developers can achieve more robust code review processes.
Practical Implementation Steps1. Generate initial code suggestions through Claude/Anthropic 2. Deploy local models like DeepSeek via Ollama 3. Conduct targeted code reviews for specific functions or modules 4. Leverage multiple models to offset individual limitations
Future OutlookAs local models become increasingly prevalent, the dual model approach gains significance. While not infallible, this framework provides a more comprehensive approach to AI-assisted development by distributing review responsibilities across multiple models with complementary strengths.
Best PracticesMaintain developer oversight throughout the process, treating AI suggestions similarly to Stack Overflow solutions that require careful review before implementation. Combine Claude's strong artifact generation capabilities with local models through Ollama for optimal results.
ConclusionThe dual model context review approach represents an evolution in AI-assisted development, offering a more nuanced and reliable framework for code generation and review. By acknowledging and planning for model limitations, developers can create more robust and reliable software solutions.
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Accelerating AI "Profit to Zero": Lessons from Open Source
Key Themes* Drawing parallels between open source software (particularly Linux) and the potential future of AI development * The role of universities, nonprofits, and public institutions in democratizing AI technology * Importance of ethical data sourcing and transparent training methods
Main Points DiscussedOpen Source Philosophy* Good technology doesn't necessarily need to be profit-driven * Linux's success demonstrates how open source can lead to technological innovation * Counter-intuitive nature of how open collaboration drives progress
Ways to Accelerate "Profit to Zero" in AI1. LLM Training Recipes * Companies like Deep-seek and Allen AI releasing training methods * Enables others to copy and improve upon existing models * Similar to Linux's collaborative improvement model 1. Binary Deploy Recipes * Packaging LLMs as downloadable binaries instead of API-only access * Allows local installation and running, similar to Linux ISOs * Can be deployed across different platforms (AWS, GCP, Azure, local data centers) 1. Ethical Data Sourcing * Emphasis on consensual data collection * Contrast with aggressive data collection approaches by some companies * Potential for community-driven datasets similar to Wikipedia 1. Free Unrestricted Models * Predicted emergence by 2025-2026 * No license restrictions * Likely to be developed by nonprofits and universities * European Union potentially playing a major role
Public Education and Infrastructure* Need to educate public about alternatives to licensed models * Concerns about data privacy with tools like Co-pilot * Importance of local processing vs. third-party servers * Role of universities in hosting model mirrors and evaluating quality
Challenges and Opposition* Expected resistance from commercial companies * Parallel drawn to Microsoft's historical opposition to Linux * Potential spread of misinformation to slow adoption * Reference to "Halloween papers" revealing corporate strategies against open source
Looking Forward* Prediction that all generative AI profit will eventually reach zero * Growing role for nonprofits, universities, and various global regions * Emphasis on transparent, ethical, and accessible AI development
Duration: Approximately 8 minutes
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Natural Language vs Deterministic Interfaces for LLMsKey PointsNatural language interfaces for LLMs are powerful but can be problematic for software engineering and automation
Benefits of natural language:
- Flexible input handling
- Accessible to non-technical users
- Works well for casual text manipulation tasks
Challenges with natural language:
- Lacks deterministic behavior needed for automation
- Difficult to express complex logic
- Results can vary with slight prompt changes
- Not ideal for command-line tools or batch processing
Proposed Solution: YAML-Based Interface* YAML offers advantages as an LLM interface: + Structured key-value format + Human-readable like Python dictionaries + Can be linted and validated + Enables unit testing and fuzz testing + Used widely in build systems (e.g., Amazon CodeBuild)
Implementation Suggestions* Create directories of YAML-formatted prompts * Build prompt templates with defined sections * Run validation and tests for deterministic behavior * Consider using with local LLMs (Ollama, Rust Candle, etc.) * Apply software engineering best practices
ConclusionMoving from natural language to YAML-structured prompts could improve determinism and reliability when using LLMs for automation and software engineering tasks.
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LLM Market Analysis & Future PredictionsMarket Dynamics* DeepSeek disrupting LLM space by demonstrating lack of sustainable competitive advantage * LM Arena (lm.arena.ai) shows models like Gemini, DeepSeek, Claude frequently exchanging top positions * ELO rating system (used in chess/UFC) demonstrates eventual market parity
Restaurant/Chef AnalogyWhen multiple restaurants compete for one talented chef, profits flow to the chef rather than creating sustainable advantage for any restaurant - illustrating perfect competition in LLM space.
2025-2026 Predictions* Heavy investment in GPUs/expensive engineers won't provide significant advantages * Evolution similar to Linux's displacement of Solaris * Growth of local/open-source models driven by: + Data privacy/legal concerns + Data breach risks + Decreasing profit margins
ConclusionCommercial AGI models likely to give way to open-source and local alternatives, with market forces driving profits toward zero through perfect competition.
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Title: Context-Driven Development with AI Assistants
Key Points:
- Compares context-driven development to DevOps practices
- Emphasizes using AI tools for project-wide analysis vs line-by-line assistance
- Focuses on feeding entire project context to AI for specific insights
- Highlights similarities with CI/CD feedback loops
- Positions this approach as non-controversial use of AI coding assistants
Main Arguments:
- AI tools work best with full project context rather than isolated code completion
- Developer maintains control over which AI suggestions to implement
- Similar to DevOps feedback loops but for code quality and improvements
- Works equally well with open-source and proprietary AI tools
Key Applications:
- Code reviews
- Test coverage analysis
- Documentation improvements
- Feature development guidance
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Title: The Case for Makefiles in Modern Development
Key Points:
- Makefiles provide consistency between development and production environments
- Primary benefit is abstracting complex commands into simple, uniform recipes
- Particularly valuable for CI/CD pipelines and cross-language projects
- Makefiles solve real-world production problems through command abstraction
- Common commands like make install and make lint work consistently across environments
Main Arguments:
- While modern build tools (like Cargo for Rust) are powerful, Makefiles still serve an important role in production environments
- Makefiles prevent subtle bugs caused by environment-specific command variations
- They're especially useful when projects combine multiple languages/tools (Rust, XML, YAML, JavaScript, SQL)
- Linux ubiquity means Make is reliably available on most servers
Balanced Perspective:
- Not advocating Makefiles for all scenarios
- Acknowledges limitations of older tools
- Emphasizes choosing tools based on specific project needs
- Draws parallel to other standard Unix tools (Vim, Bash) - limitations balanced by ubiquity
Key Takeaway: Makefiles remain valuable for production-first development, particularly in enterprise environments with complex CI/CD requirements, despite newer alternatives.
Context: Discussion focuses on practical software engineering decisions, emphasizing the importance of considering production environment needs over local development preferences.
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Update 12/26/2024 on the Pragmatic AI Labs Platform development lifecycle. Thanks again for all of the new subscribers. A few things I mention in the video update:
- Almost every day a new course, lab, or feature will appear most days in 2025.
- We don't just teach, we do. Watch us build a world class learning platform day by day by joining the platform and doing exactly what we are teaching
- There is a sense of urgency and mission with our platform. We know we can do better than what exists and we are rolling up our sleeves and doing it one day at a time. Thank you for the support!
Support our mission by joining here: https://ds500.paiml.com/subscribe.html
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Introducing the Pragmatic AI Labs Learning Platform with Noah GiftEpisode SummaryIn this episode, Noah Gift, co-founder of Pragmatic AI Labs, introduces their innovative new learning platform. Drawing from their experience teaching millions of students worldwide, including at prestigious institutions like UC Berkeley, Duke, and Northwestern, Pragmatic AI Labs has developed a unique educational platform that combines comprehensive content with interactive labs and hands-on learning experiences.
Key Highlights* Platform developed in-house by practicing educators * Over two master's degrees worth of content * Interactive bootcamps and hands-on labs * Weekly platform updates and new feature releases * Built using Rust programming language * Focus on practical job skills and upskilling
Detailed Show NotesAbout Pragmatic AI Labs* Founded by experienced educators with a track record of teaching at elite institutions * Platform built based on identified learning gaps and student needs * Commitment to continuous innovation and development * Focus on teaching at scale while maintaining quality
Platform Features1. Content Library
* Comprehensive course materials equivalent to two master's degrees
* Content previously validated on major learning platforms
* Specialized focus on data science, machine learning, and computer science
-
Interactive Learning
- Custom-built interactive labs
- Hands-on coding experiences
- Badge system for achievement tracking
- Weekly feature updates and improvements
-
Featured Course Highlight
-
Rust Fundamentals course
- Structured week-by-week navigation
- Clear learning objectives
- Comprehensive lesson materials
- Key terms and concept definitions
Platform Development Philosophy* Built entirely in-house using Rust * Continuous development and feature additions * Focus on practical, job-relevant skills * Commitment to long-term platform growth * Experience with scaling to millions of users
How to Get Involved* Visit the DS500 platform page * Create an account through the "Join Now" option * Explore available courses and interactive content * Provide feedback to help improve the platform
Target Audience* Students seeking practical tech skills * Professionals looking to upskill * Anyone interested in data science, machine learning, or computer science * Learners who prefer hands-on, interactive experiences
About the SpeakerNoah Gift is a co-founder of Pragmatic AI Labs and has extensive experience teaching at prestigious institutions including UC Berkeley, Duke, and Northwestern. His approach combines practical industry experience with academic rigor to create effective learning experiences.
Tags: Education Technology, Online Learning, Programming, Data Science, Machine Learning, Professional Development, Rust Programming
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تستكشف هذه الحلقة الرحلة المذهلة لـ DevOps، متتبعة جذورها من مبادئ التصنيع اليابانية إلى الحوسبة السحابية الحديثة. نتعمق في كيفية تشكيل فلسفة كايزن من تويوتا والمنهج العلمي لممارسات DevOps اليوم، ونفحص مبادئ AWS DevOps الستة الأساسية التي تقود تطوير البرمجيات الحديثة.
ملاحظات المقدمالمقدمة التشويقية* ابدأ بالتأثير الحديث: "في قلب DevOps الحديث يكمن تبني السحابة" * التشويق للرابط المدهش مع تويوتا والتصنيع الياباني
الأقسام الرئيسية1. الأساس التاريخي (5 دقائق)
* تقديم مفهوم كايزن
* الارتباط بنظام إنتاج تويوتا
* دورة خطط-نفذ-تحقق-اعمل
-
ثورة الخمسة لماذا (7 دقائق)
- شرح التقنية
- مشاركة زاوية فضول الأطفال
- مثال واقعي لتصحيح الأخطاء
-
تحليل عميق لـ AWS DevOps (12 دقيقة)
-
شرح CI/CD
- البنية التحتية كرمز
- تكامل الأمان
- المراقبة والتسجيل
-
التطبيق الحديث (4 دقائق)
-
فوائد الحوسبة السحابية
- نقاط التفاعل البشري
- الآثار المستقبلية
نقاط الختام* التأكيد على التحسين المستمر * إبراز التطوير السحابي الأصلي * دعوة للعمل لتطبيق ممارسات DevOps
الهاشتاغات#DevOps, #AWS, #الحوسبة_السحابية, #كايزن, #طريقة_تويوتا, #التكامل_المستمر, #DevSecOps, #الهندسة, #تطوير_البرمجيات, #بودكاست_تقني, #السحابة_الأصلية, #الأتمتة, #القيادة_التقنية, #الابتكار
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主持人提示开场引子* 从现代影响开始："现代DevOps的核心是对云计算的拥抱" * 预告与丰田和日本制造业的惊人联系
关键环节1. 历史基础 (5分钟)
* 介绍改善概念
* 丰田生产系统的联系
* 计划-执行-检查-行动循环
-
五个为什么革命 (7分钟)
- 解释技术
- 分享儿童般好奇心的角度
- 实际调试案例
-
AWS DevOps深度剖析 (12分钟)
-
CI/CD说明
- 基础设施即代码
- 安全集成
- 监控和日志记录
-
现代实施 (4分钟)
-
云计算优势
- 人机交互点
- 未来影响
结束要点* 强调持续改进 * 突出云原生开发 * DevOps实践行动号召
话题标签#DevOps, #AWS, #云计算, #改善, #丰田之道, #持续集成, #DevSecOps, #工程, #软件开发, #科技播客, #云原生, #自动化, #技术领导力, #创新
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Resumen del EpisodioTítulo: Evolución DevOps: De Toyota a la Nube
Episodio: #147
Duración: ~30 minutos
Este episodio explora el fascinante viaje de DevOps, trazando sus raíces desde los principios de manufactura japoneses hasta la computación en la nube moderna. Profundizamos en cómo la filosofía Kaizen de Toyota y el método científico dieron forma a las prácticas actuales de DevOps, y examinamos los seis principios fundamentales de DevOps de AWS que impulsan el desarrollo de software moderno.
Notas del PresentadorApertura* Comenzar con el impacto moderno: "En el corazón del DevOps moderno está la adopción de la nube" * Adelantar la sorprendente conexión con Toyota y la manufactura japonesa
Segmentos Clave1. Fundamento Histórico (5 mins)
* Introducir el concepto Kaizen
* Conexión con el Sistema de Producción Toyota
* Ciclo Plan-Do-Check-Act
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La Revolución de los 5 Por Qués (7 mins)
- Explicar la técnica
- Compartir el ángulo de la curiosidad infantil
- Ejemplo real de depuración
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Análisis Profundo de AWS DevOps (12 mins)
-
Explicación de CI/CD
- Infraestructura como Código
- Integración de seguridad
- Monitoreo y registro
-
Implementación Moderna (4 mins)
-
Beneficios de la computación en la nube
- Puntos de interacción humana
- Implicaciones futuras
Puntos de Cierre* Enfatizar la mejora continua * Destacar el desarrollo nativo en la nube * Llamado a la acción para implementar prácticas DevOps
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Speaker NotesOpening Hook* Start with the modern impact: "At the heart of modern DevOps is an embrace of the cloud" * Tease the surprising connection to Toyota and Japanese manufacturing
Key Segments1. Historical Foundation (5 mins)
* Introduce Kaizen concept
* Toyota Production System connection
* Plan-Do-Check-Act cycle
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The 5 Whys Revolution (7 mins)
- Explain the technique
- Share the child-like curiosity angle
- Real-world debugging example
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AWS DevOps Deep Dive (12 mins)
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CI/CD explanation
- Infrastructure as Code
- Security integration
- Monitoring and logging
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Modern Implementation (4 mins)
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Cloud computing benefits
- Human interaction points
- Future implications
Closing Points* Emphasize continuous improvement * Highlight cloud-native development * Call to action for implementing DevOps practices
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Código Limpio en Python: La Clave para un Desarrollo de Software ExitosoResumen del EpisodioEn este episodio, exploramos la importancia de escribir código limpio, testeable y de alta calidad en Python. Basándonos en un ensayo de Noah Gift de 2010, discutimos cómo el enfoque en la calidad del código desde el principio puede llevar a proyectos de software más exitosos y mantenibles.
Puntos Clave1. La complejidad es el enemigo: Controlar la complejidad es esencial en el desarrollo de software. 2. Pensamiento proactivo: Los desarrolladores exitosos piensan en la testabilidad y mantenibilidad desde el inicio. 3. Desarrollo guiado por pruebas: Escribir pruebas antes o durante el desarrollo da forma al código de manera positiva. 4. Métricas de calidad: * Cobertura de código * Complejidad ciclomática 5. Herramientas útiles: * Nose para pruebas unitarias y cobertura de código * Pylint y Pygenie para análisis estático
La Importancia de la Complejidad Ciclomática* Desarrollada por Thomas J. McCabe en 1976 * Mide el número de caminos independientes en el código * Se recomienda mantener la complejidad por debajo de 10 * Alta complejidad se correlaciona con mayor probabilidad de errores
ConclusiónEl desarrollo de software de calidad requiere un enfoque consciente en la testabilidad y la simplicidad. Las herramientas de análisis y las pruebas automatizadas son aliados valiosos, pero el verdadero éxito viene de una mentalidad enfocada en la calidad desde el principio.
Recursos Adicionales* Herramienta de integración continua: Hudson * Libros recomendados: + "Software Tools" de Brian Kernighan + "The Pragmatic Programmer" de Andrew Hunt y David Thomas
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Episode NotesWhat is Amazon Bedrock?* Fully managed service offering foundation models through a single API * Described as a "Swiss Army knife for AI development"
Key Components of Bedrock1. Foundation Models
* Pre-trained AI models from leading companies
* Includes models from AI21 Labs, Anthropic, Cohere, Meta, and Amazon's Titan
-
Unified API
- Single interface for interacting with multiple models
- Simplifies integration and maintenance
-
Fine-tuning Capabilities
-
Ability to customize models for specific use cases
-
Security and Compliance
-
Built with AWS's security standards
Best Practices for Using Bedrock1. Modular Design
* Create separate functions or classes for different Bedrock operations
* Enhances testability and maintainability
-
Error Handling
- Implement robust error handling with try-except blocks
- Proper logging of errors
-
Configuration Management
-
Store Bedrock configurations (e.g., model IDs) in separate files
- Facilitates easy updates and switches between models
-
Testing
-
Write unit tests for Bedrock integration
- Mock API responses for comprehensive testing
-
Continuous Integration
-
Set up CI/CD pipelines including Bedrock tests
- Ensures ongoing functionality with code changes
Key Takeaways* Focus on creating reliable, maintainable, and scalable AI systems * Apply clean coding principles to Bedrock integration * Balance functionality with long-term code quality
This episode provides a solid foundation for developers looking to leverage Amazon Bedrock in their projects while maintaining high standards of code quality and testability.
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Episode Notes1. The Complexity Challenge
* Software development is inherently complex
* Quote from Brian Kernigan: "Controlling complexity is the essence of software development"
* Real-world software often suffers from unnecessary complexity and poor maintainability
-
Rethinking the Development Process
- Shift from reactive problem-solving to thoughtful, process-oriented development
- Importance of continuous testing and proving that software works
- Embracing humility, seeking critical review, and expecting regular refactoring
-
The Pitfalls of Untested Code
-
Dangers of the "mega function" approach
- How untested code leads to uncertainty and potential failures
- The false sense of security in seemingly working code
-
Benefits of Test-Driven Development
-
How writing tests shapes code structure
- Creating modular, extensible, and easily maintainable code
- The visible difference in code written with testing in mind
-
Measuring Code Quality
-
Using tools like Nose for code coverage analysis
- Introduction to static analysis tools (pygenie, pymetrics)
- Explanation of cyclomatic complexity and its importance
-
Cyclomatic Complexity Deep Dive
-
Definition and origins (Thomas J. McCabe, 1976)
- The "magic number" of 7±2 in human short-term memory
- Correlation between complexity and code faultiness (2008 Enerjy study)
-
Continuous Integration and Automation
-
Brief mention of Hudson for automated testing
- Encouragement to set up automated tests and static code analysis
-
Concluding Thoughts
-
Testing and static analysis are powerful but not panaceas
- The real goal: not just solving problems, but creating provably working solutions
- How complexity, arrogance, and disrespect for Python's capabilities can hinder success
Key Takeaways* Prioritize writing clean, testable code from the start * Use testing to shape your code structure and improve maintainability * Leverage tools for measuring code quality and complexity * Remember that the goal is not just to solve problems, but to create reliable, provable solutions
This episode provides valuable insights for Python developers at all levels, emphasizing the importance of thoughtful coding practices and the use of testing to create more robust and maintainable software.
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- Introduction: The host begins by highlighting the need to approach AI ethics from an "externality first" perspective, focusing on the impact on humans rather than just economic indicators like GDP.
- Historical Context: The episode explores the French Revolution as a case study for understanding the emergence of human rights.
- The revolution was fueled by systemic issues like feudalism, poverty, and hunger, along with the spread of new ideas about democracy.
- While the revolution led to significant advancements in human rights, it also had negative consequences, including mob rule, violence, and political purges fueled by misinformation.
- Digital Feudalism: The sources draw a parallel between feudalism and the current digital landscape:
- Peasants in feudal societies were tied to the land, while individuals today are often trapped on digital platforms.
- Data scraping, dark patterns, and the gig economy limit user control and create exploitative labor conditions.
- Echo chambers, social media addiction, and the prevalence of clickbait contribute to an "intellectual handicap" among the population.
- Surveillance Capitalism: The episode discusses the concept of "surveillance capitalism," a business model that profits from mass data collection and manipulation:
- This model threatens democracy, modifies behavior through nudges, and grants corporations significant power over governments and citizens.
- The sources emphasize that collecting data "just because you can" violates privacy rights.
- The Tragedy of the Generative AI Commons: The sources argue that generative AI exacerbates the "tragedy of the commons":
- Intellectual property theft, job displacement, and the erosion of quality control create negative externalities that impact society.
- The lack of recognition and attribution for creators demotivates them and raises ethical concerns.
- Game Theory and AI: The episode examines the application of game theory concepts, like the prisoner's dilemma, to understand the potential pitfalls of AI development.
- A race to the bottom can occur when companies prioritize short-term profits over ethical considerations, leading to the proliferation of low-quality, potentially harmful content.
- Negative Externalities: The sources emphasize the need to consider the unintended consequences of AI development, even when those consequences are not immediately apparent.
- Tech Propaganda: The episode explores the role of propaganda in shaping public perception of AI:
- Tactics like FOMO (Fear of Missing Out), naive utopianism, superficial media coverage, and the glorification of "disruption" contribute to a distorted understanding of AI's potential benefits and risks.
- Digital Rights of Humans: The episode concludes by outlining key digital rights that should be protected in the age of AI:
- Right to Consent: Individuals should have control over their data and intellectual property, with opt-in consent required for its use.
- Right to Privacy: Individuals should have the right to a life free from surveillance capitalism, including protection from dragnet surveillance, continuous location tracking, and the exploitation of biometric data.
- Right to Freedom from Addiction: Technology should be designed to empower, not exploit, users, minimizing addictive features.
- Right to Protection from Algorithmic Harm: Individuals should be protected from the negative consequences of algorithms, such as misinformation spread, price fixing, and discriminatory practices.
- Right to a Digital Commons: The digital space should be protected from exploitation and destruction, ensuring access to information and opportunities for all.
- Right to Real Information: Individuals should have access to factual information and be protected from propaganda and misinformation.
- Right to a Non-Exploitative Business Model: Business models that depend on the violation of digital rights are inherently flawed and need to be reformed.
- Call to Action: The episode encourages listeners to advocate for digital rights that prioritize human well-being, holding corporations and governments accountable for the ethical development and deployment of AI.
- Outro: The host leaves listeners with a question: "What role can we play in shaping a future where AI serves humanity?"
AI Generation Disclaimer: This podcast title, episode summary, and episode notes were generated with the assistance of an AI program, using information provided in the sources. While every effort has been made to ensure accuracy and relevance, it is recommended that listeners independently verify any information presented.
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What is Serverless? Serverless computing is a modern approach to software development that optimizes efficiency by only running code when needed, unlike traditional always-on servers. * Analogy: A motion-sensing light bulb in a garage only turns on when motion is detected. Similarly, serverless functions are triggered by events and automatically scale up and down as required. * Benefits: + Efficiency: Only pay for the compute time used, billed in milliseconds. + Scalability: Applications scale automatically based on demand. + Reduced Management Overhead:* No need to manage servers, AWS handles the infrastructure.
Function as a Service (FaaS) FaaS is a fundamental building block of serverless technology. * It involves deploying individual functions that perform a specific task, like an "add" function. * AWS Lambda is a popular example of a FaaS platform. * Benefits: + Simplicity: Easy to understand and manage individual functions. + Scalability: Functions can be scaled independently based on demand. + Cost-effectiveness:* Only pay for the compute time used by each function.
Why Rust for Serverless Data Engineering? Rust's performance, safety, and deployment characteristics make it well-suited for serverless. * Analogy: Building a durable, easy-to-clean cup (Rust) versus a quick, disposable cup (Python). * Benefits: + Performance: Rust is a high-performance language, leading to faster execution times and potentially lower costs. + Cost-effectiveness: Rust's low memory footprint can significantly reduce AWS Lambda costs as you are charged based on memory usage. + Safety: Rust's strong type system and memory safety features help prevent errors and improve code reliability. + Easy Deployment: Cargo Lambda simplifies the process of building, testing, and deploying Rust functions to AWS Lambda. + Maintainability:* Rust's features promote the creation of code that is easier to maintain and less prone to errors in the long run.
Introducing Cargo Lambda Cargo Lambda is a framework designed to simplify the development, testing, and deployment of Rust functions to AWS Lambda. * Benefits: + Leverages Rust's advantages: Allows developers to utilize Rust's performance, safety, and efficiency for serverless functions. + Easy Deployment: Streamlines the process of deploying Rust functions to AWS Lambda. + Local Testing: Provides tools for testing and debugging functions locally before deploying. + Custom Runtime: Optimizes the AWS Lambda Rust runtime for specific Rust capabilities. + Ecosystem Integration:* Seamless integration with other AWS services and the Rust ecosystem.
Building a Tunnel Game with Cargo Lambda* The sources provide a step-by-step guide to building a simple "tunnel game" using Cargo Lambda. * The game demonstrates how to receive and process requests, generate random responses, and deploy a Rust function to AWS Lambda.
Comparing Runtimes: Rust, Ruby, and Node.js The sources compare the performance of Rust, Ruby, and Node.js in AWS Lambda, highlighting the impact of memory usage on cost. * Rust exhibits significantly lower memory usage compared to Ruby and Node.js, leading to potential cost savings.*
Key Takeaways Serverless computing offers a more efficient and scalable approach to software development. * FaaS, specifically AWS Lambda, is a powerful tool for building serverless applications. * Rust, with its performance, safety, and cost-effectiveness, emerges as an excellent choice for serverless data engineering.* * Cargo Lambda simplifies the development and deployment of Rust functions on AWS Lambda.
Podcast Notes: Invite a guest expert in Rust and serverless computing for a deeper discussion. * Provide code examples and demonstrations of Cargo Lambda in action. * Discuss real-world use cases of Rust in serverless data engineering. * Share tips and resources for getting started with Rust and Cargo Lambda.*
This response summarizes the key concepts presented in your provided audio source excerpts. Please let me know if you have any further questions or need additional information.
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Episode Notes Introduction to Cargo Lambda*
+ Interacts with AWS Lambda ecosystem from the terminal
+ Enables native running, building, and deployment of Lambda functions
+ No need for containers or VMs
-
Installation Options
- Homebrew (recommended for macOS and Linux)
- Scoop for Windows
- Docker and Nix as alternatives
- Binary release or building from source
-
Getting Started
-
Use
cargo lambda newto create a project - Directory structure includes package management, default code, compiler, and linter
cargo lambda watchfor immediate code writingcargo lambda invokefor testing with JSON payloads-
Web Framework Support
-
Ability to expose microservices with HTTP interfaces
-
Deployment Process
-
cargo lambda build --releasefor building (including ARM64 support) cargo lambda deployfor straightforward deployment-
Additional Features
-
Verbose mode and tracing options available
- Integration with GitHub Actions and AWS CDK
-
Advantages of Cargo Lambda
-
Leverages the robust Rust ecosystem
- Modern package management with Cargo
- Potentially easier than scripting languages for Lambda development
Key Takeaways1. Cargo Lambda offers a superior method for interacting with AWS Lambda compared to scripting languages. 2. The tool provides a streamlined workflow for creating, testing, and deploying Lambda functions. 3. It leverages the Rust ecosystem, offering modern package management and development tools. 4. Cargo Lambda supports both function-based and web framework approaches for Lambda development. 5. The ease of use and integration with AWS services make it an attractive option for Lambda developers.
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Pragmatic AI Labs Blog - What is Cargo Lambda
What is Cargo Lambda?* A framework for building tools and workflows for Rust on AWS Lambda
Key Benefits1. Rust Performance
* Allows writing AWS Lambda functions in Rust
* Provides amazing performance and low cold start times
* Leverages modern compilation features of Rust
-
Type Safety
- Utilizes Rust's strong type system
- Helps catch errors at compile time
- Reduces runtime errors in production
-
Memory Safety
-
Implements Rust's Ownership model
- Prevents common bugs like null pointer dereferences
- Eliminates data races without a garbage collector
-
Easy Deployment
-
Simplifies the process of building, testing, and deploying Rust functions to AWS Lambda
- Leverages Rust's modern binary-based features for optimized and cross-compiled binaries
-
Local Testing
-
Provides tools for running and debugging Lambda functions locally
- Enhances the development and prototyping process
-
Custom Runtime
-
Leverages the AWS Lambda Rust runtime
- Allows optimization for Rust's unique performance capabilities
-
Ecosystem Integration
-
Easy integration with other AWS services
- Seamless connection to the broader Rust ecosystem
-
Resource Efficiency
-
Utilizes Rust's naturally low memory footprint
- Potentially 70-80% less memory usage compared to languages like Python
- Cost-effective for data engineering pipelines
-
Cross-compilation Support
-
Enables building Lambda functions for different architectures
- Allows targeting ARM for cost savings on high-performance functions
- Productivity
- Streamlines the development workflow for Rust
- Combines powerful features with time-saving processes
ConclusionCargo Lambda offers a compelling solution for developers looking to leverage Rust's power in serverless environments, providing a unique combination of performance, safety, and ease of use.
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Function as a Service (FaaS): Core Building Block of Serverless TechnologyWhat is FaaS?* Simplest unit of work for building applications, microservices, or event-driven protocols * Basic workflow: Input → Logic → Output
Characteristics of FaaS* Simple and easily understandable * Highly scalable * Quick response time
Popular FaaS Framework: AWS Lambda* Can be attached to various services: + S3 notifications (e.g., file uploads) + SQS (Simple Queue Service) messages * Enables building infinitely scalable services with small response times
Best Languages for Serverless/FaaS1. Rust 2. Go
Advantages of Modern Compiled Languages for FaaS* Speed * Safety * Optimal deployment characteristics * Millisecond response and invocation times * Low energy usage
Key Considerations for FaaS Development* Focus on maintenance over ease of building * Optimize for low costs (financial and energy) * Consider total cost of service over time
TakeawayWhen developing Function as a Service applications, prioritize long-term efficiency, maintenance, and cost-effectiveness over initial development ease. Choose languages and practices that support these goals in a serverless environment.
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Build a cup vs wash a cup blog post
Building vs. Washing a Cup: Rust vs. Scripting LanguagesKey Points:* Analogy: Building a cup (initial development) vs. washing a cup (maintenance) * Rust represents a well-crafted cup, while Python represents a quickly made, crude cup
Advantages of Rust:1. Optimized for long-term maintenance 2. Compiler catches bugs early: * Type errors * Syntax errors * Concurrency issues 3. Better packaging and deployment 4. Improved energy efficiency 5. Smaller carbon footprint
Disadvantages of Scripting Languages (e.g., Python):1. Easier initial development, but potential long-term issues 2. Packaging often an afterthought 3. Slower package performance 4. No compiler to catch certain types of bugs
Considerations for Choosing a Language:* Long-term maintenance costs * Energy efficiency * Carbon footprint * Deployment process * Overall cost (human labor and cloud resources)
Takeaway:When selecting a programming language, consider factors beyond initial ease of use. Languages like Rust may require more upfront effort but can provide significant long-term benefits in terms of maintenance, performance, and reliability.
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Understanding Serverless ComputingWhat is AWS Lambda
Notes:Introduction to Serverless
- New paradigm in cloud computing
- Contrasts with pre-cloud, always-running systems
Inefficiency of Traditional Models
- Example: Apache web service running constantly
- Analogy: Lights always on in a house
Characteristics of Serverless Computing
- Stateless
- Event-driven
- Automatically scalable
- "Logic to live" concept
Light Bulb Analogy
- Manual invocation (switch)
- Timer-based activation
- Sensor-triggered (motion, garage door)
Simplicity in Coding
- Functions in various languages (Python, Rust, Go)
- Input-process-output model
Efficiency and Use Cases
- Low latency workloads
- Data engineering
- Modern cloud-native workflows
Example of Serverless Platform
- AWS Lambda mentioned as popular example
Key Takeaways:* Serverless computing offers more efficient resource utilization than traditional models * It's event-driven and scales automatically * Simplifies coding by focusing on function-based logic * Well-suited for modern cloud applications and data engineering tasks
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Broken Economic Models for HumanityKey Concepts:1. Surveillance Capitalism
* Definition by Shoshana Zuboff
* Extracts and monetizes human experience data
* Concentrates wealth, knowledge, and power
* Threatens human nature and democracy
-
Externality First Capitalism
- Proposed solution to create markets for social good
- Examples:
- Carbon pricing in services
- Media platform taxation based on factual content
- Tax credits for repairable technology
- Taxation of addictive technology profits
- Corporate and individual wealth tax
- Right to repair initiatives
-
Game Theory and AI
-
Tragedy of the Commons applied to GenAI
- Internet as a public commons
- Data collection without consent destroys the commons
-
Privacy and Power
-
Importance of privacy in protecting freedom
- Data collection's impact on society
- Need for action to reclaim privacy
-
Optimizing for Humans
-
Critiques of current business climate
- Anti-patterns in current systems:
- Rapid growth
- Addiction
- Income inequality
- Centralized systems
- Focus on human welfare over GDP
- Importance of environmental protection
Key Takeaways:* Current economic models, especially surveillance capitalism, pose significant threats to human rights and societal well-being. * Solutions should focus on creating incentives for social good and addressing negative externalities. * Privacy is crucial for maintaining individual freedom and societal health. * Economic systems should prioritize human welfare and environmental protection over unchecked growth and profit.
Action Items:1. Research and support initiatives that promote "Externality First Capitalism" 2. Advocate for stronger privacy protections and data rights 3. Support right to repair movements and sustainable technology practices 4. Engage in discussions about redefining economic success metrics to prioritize human welfare
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Human Rights: From French Revolution to Digital AgeThe Age of Revolutions: A Perfect StormThe French Revolution emerged from a convergence of systemic issues and random events:
- Feudalism's oppressive structure
- Widespread poverty and hunger
- Emerging ideas of democracy
- Influence of Thomas Paine's "Common Sense"
- Inspiration from the American Revolution
- Rise of mass printing and pamphleteers
The Rights of Man: Reshaping SocietyThe French Revolution brought forth the concept of human rights, influencing democracy globally:
- Liberty
- Property ownership
- Personal security
- Natural rights
- Freedom
- Resistance to oppression
- National authority over individual rulers
Note: Major limitations existed for women and slaves
The Dark Side: Mob Rule and NapoleonNegative aspects of the revolution included:
- Violent and irrational mob rule
- Misinformation spread through pamphlets
- Innocent victims of violence
- Political purity purges
- Power vacuum leading to Napoleon's rise
Feudalism: A System of ExploitationHuman rights were non-existent under feudalism:
- Limited education
- Forced labor
- Arbitrary justice
- No property rights
Digital Feudalism: A Modern ParallelToday's digital landscape mirrors feudal exploitation:
- No opt-out options for data scraping
- Dystopian gig economy labor
- Opaque platform policies
- Data serfdom trapping users
- Intellectual handicap through echo chambers and addiction
Surveillance Capitalism: Profiting from Human DataA business model built on mass surveillance:
- Threat to informed democracy
- Behavior modification through nudges
- Algorithmic governance superseding nations
- Asymmetrical power of corporations
- Vulnerability to data breaches
The Need for Human Digital RightsPrioritizing humans over corporations and technology:
- Data and intellectual property should belong to individuals (opt-in use only)
- Rejection of exploitative business models
- Right to a digital commons
- Right to live free from addiction and algorithmic harm
As we navigate the digital age, it's crucial to learn from history and establish robust digital rights to protect human autonomy and dignity.
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Tech Propaganda: An Introduction to Critical Thinking in TechnologyEpisode Notes1. FOMO (Fear of Missing Out)* Definition: Rushing to adopt new technologies without clear benefits * Examples: + Implementing GenAI without clear ROI just because competitors are doing it + Skill development driven by fear of obsolescence + VCs worried about missing the next big thing
- Naive Utopianism* Definition: Assuming all technology is inherently good
-
Examples:
- Believing more smartphone scrolling is always better
- Expecting social media to lead to world peace
- Promoting UBI or crypto as universal solutions
- Assuming AI can completely replace teachers
-
Disruption and Technological Solutionism* Definition: Ignoring negative consequences of tech solutions
-
Key point: Tendency to overlook negative externalities
-
"Selling Two Day Old Fish"* Definition: Resisting improvements to maintain profitable but outdated products/services
-
Examples:
- Exaggerating job market demand for outdated skills
- Appealing to authority (big tech companies)
- Dismissing newer technologies as unnecessary or overly complex
- Claiming established technologies aren't actually old/slow
-
Superficial Media* Definition: Promoting shallow or misleading information about technology
-
Examples:
- Media monetizing via supplements
- Conspiracy theory forums
- Inexperienced podcast hosts discussing complex topics
- Making sensational predictions about future tech with little evidence
- Oversimplifying complex topics
-
Push to Disrupt* Definition: Overconfidence in technology's ability to solve complex problems
-
Examples:
- "Figure out the business model later" mentality
- Pushing products to market prematurely
- Ignoring negative externalities
- Dismissing critics as "not understanding the vision"
-
Billionairism* Definition: Excessive admiration of tech billionaires and their perceived expertise
-
Examples:
- Equating extreme wealth with universal expertise
- Idolizing tech billionaires as infallible visionaries
- Romanticizing the "Harvard/Stanford dropout genius" narrative
- Ignoring the role of luck vs. skill
- Overemphasizing individual genius over team efforts
-
Irrational Exceptionalism* Definition: Unrealistic beliefs about a startup's chances of success
-
Examples:
- "We're different from other startups that fail"
- "Weekends are a social construct"
- Obsession with "changing the world"
- Rationalizing present hardships for imagined future gains
- Dismissing industry-wide failure rates
- Glorifying extreme effort and sacrifice
-
Double Down* Definition: Making increasingly grand claims to distract from unfulfilled promises
-
Examples:
- Promising self-driving cars "next year", then pivoting to Mars travel
- Deflecting from current AI model flaws with promises of future sentience
-
Trojan Source* Definition: Open source projects that later switch to commercial licensing
-
Examples:
- "Rug pull" strategy in open source
- Using community labor before pivoting to commercial model
-
"Generous Pour" Ethical Framing* Definition: Highlighting easy ethical actions while ignoring larger issues
-
Examples:
- Claiming unbiased AI training sets while hiding addictive design
- Emphasizing harm reduction in AI outputs while ignoring IP theft
-
Business Model Circular Logic* Definition: Exploiting legal grey areas and claiming they're essential to the business model
- Examples:
- Justifying use of pirated data for AI training
- Creating unfair competition by ignoring regulations (e.g., taxi services, hotels)
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source: By Milky - Museum of the French Revolution, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=78860432
The French Revolution is perhaps the most crucial event in the history of the world. A population violently overthrew a monarchy and developed important concepts of both individual liberty and democracy. It also showed the violent and horrific excesses of mob rule, something the world struggles with even today. Both helpful ideas and toxic misinformation spread through pamphleteers with often deadly consequences. In the ultimate irony the most famous of pamphleteers, Jacques Hebert, who often advocated for guillotining, was a victim of it himself. [1]
Perhaps the most important contribution though was the “Declaration of the Rights of Man and of the Citizen”. The essence of the declarations was that French citizens had the right to liberty, property and national authority vs royalty authority. These replaced feudal ideas of rigid hierarchy, land for service, and peasant exploitation.
In the digital age, there is a new feudalism, and similar problems exist. A Digital Rights of Humans could be phrased as follows:
- Data and Intellectual Property belongs to the creator and cannot be exploited without opt-in consent.
- Humans have the right to privacy and a life free of surveillance capitalism.
- Humans own their own biometric data and it cannot be owned and stored by others
- Humans have the right to a life free of addictive technologies
- Humans have the right to protection from algorithmic harm (spreading conspiracy theories that hurt life and property). These include systems that create negative externalities like price fixing of rent.
- Humans have the right to a digital commons free from exploitation and destruction
- Humans have the right to real information, not propaganda
- A business model that depends on exploitation of a human’s digital rights is defective by design
Reference1. Popkin, J., December 10, 2019, A New World Begins 2. Davidson, I., 2016, The French Revolution 3. The French Revolution, The Rest is History Podcast
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My System: https://kfocus.org/spec/spec-m2
Ecosystem: https://kfocus.org/land/business
Live Stream: The Rise of Linux Desktop for Professionals
Join industry experts Noah Gift and Michael Mikowski for an insightful discussion on why 2024 is being hailed as "the year of the Linux desktop" and how professionals can successfully transition to this powerful alternative.
What We'll Cover:
• Why professionals are moving away from proprietary operating systems
• The surprising advantages of Linux desktop for productivity and privacy
• Debunking common myths about Linux desktop
• How to choose the right Linux setup for your needs
• Practical tips for migrating from Windows or macOS
• The importance of specialized Linux desktop providers
Your Hosts:
Noah Gift: Adjunct Professor at Duke & UC Davis, founder of Pragmatic AI Labs
Michael Mikowski: Product designer, entrepreneur, Linux desktop expert
Whether you're an IT pro, developer, or privacy-conscious individual, discover how Linux desktop can offer a stable, powerful, and privacy-respecting alternative to mainstream operating systems.
🔴 Join us live for an interactive Q&A session!
LinuxDesktop #TechTalk #OpenSource
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📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
00:00:01 - Introduction to the concept of "pets vs. cattle" in the DevOps space
00:00:33 - Similarities between smartphones, monolithic servers, and synchronous messaging
00:01:02 - The problem with 24/7 availability and bi-directional feedback loops
00:01:50 - The addictive nature of smartphones and their impact on well-being
00:02:51 - The evolution of software engineering practices and the rise of serverless architecture and event-based messaging
00:04:37 - The benefits of a "cattle-based" approach to smartphone use
00:04:44 - Examples of batch-based operations and single-purpose devices
00:06:33 - Personal anecdotes demonstrating the liberation of breaking free from the smartphone trap
00:07:21 - Challenging listeners to question the smartphone anti-pattern and explore alternative ways of staying connected
00:07:55 - Encouraging a more mindful, balanced approach to technology in our lives
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
00:00 - Introduction: The cuckoo egg dilemma in technology 01:00 - Explanation of the cuckoo bird's behavior and the analogy to technology 02:00 - The creator's perspective: Taylor Swift as an example 02:45 - The consumer's perspective: Spending time and money on consuming 03:30 - The worker's perspective: Fixed salary and the balance between creation and consumption 04:30 - The idea of getting rid of "vampire" devices that steal our time 05:15 - Alternatives to smart technology: Dumb phones and single-purpose devices 06:00 - Replacing scrolling time with activities that prepare for creation (e.g., reading) 07:00 - Evaluating the importance of consumer-driven activities vs. research and creation 07:45 - The retraction in the smartphone revolution and protecting what we care about 08:30 - The cuckoo egg dilemma in the context of platforms scraping creator content 09:15 - The need to reshape our focus to avoid raising the "cuckoo's egg" 10:00 - Conclusion: Sharing raw ideas about the dilemma for creators and consumers
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
00:00 - Introduction: Why you need a repairable Linux laptop 01:05 - The problem with non-upgradable laptops and being locked into ecosystems 02:30 - Introducing the Framework laptop as a repairable and customizable alternative 04:15 - The benefits of being able to upgrade components and choose ports 05:40 - The Framework marketplace for swapping parts and selling components 07:00 - Recognizing the cyclical nature of technology and avoiding locked ecosystems 08:20 - Issues with Windows (costs, controversial features) and macOS (forced partnerships) 10:00 - The advantages of Linux's modular architecture and its progress over time 11:35 - The shift towards Linux among non-technical users due to dissatisfaction with commercial operating systems 13:00 - Considering the peak of commercial operating systems and the need for change 14:20 - The importance of continuous improvement (kaizen) in personal computing choices 15:40 - Moving away from lock-in strategies and opting for repairable and upgradable devices 16:50 - Getting started with Linux laptops and finding community support 18:00 - Host's personal experience with buying upgradable Linux laptops and offering assistance 19:00 - Conclusion and encouragement to explore Linux laptops
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
00:00 - Introduction: Showcasing Pragmatic AI Labs and DS500's latest offering
00:45 - Introducing the world's largest online cloud computing program
01:30 - Host's background teaching cloud computing at top universities
02:00 - Overview of the program's coverage: foundational infrastructure, serverless, agile development, cloud-native systems
02:45 - The certificate available from edX upon completion
03:15 - Course 1: Foundations of Cloud Computing
03:45 - Course 2: Virtualization, Containers, and APIs
04:15 - Course 3: Cloud Data Engineering (big data, streaming vs. batch)
04:45 - Course 4: Cloud Machine Learning Engineering and Cloud MLOps
05:15 - Course 5: Comprehensive AWS course (solutions architect, developer, machine learning, security, networking)
06:30 - Course 6: GCP basics
06:50 - Courses 7-8: Azure fundamentals
07:30 - The unique value of the program based on real-world experience and academic teaching
08:15 - Building a mastery-level portfolio with no weak spots in cloud computing
08:45 - Encouraging listeners to explore the program
09:00 - Continuous improvement (kaizen) and more programs in the pipeline
09:30 - Conclusion: Excitement about launching the biggest program to date
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
00:00 - Introduction: Launching DS500, a new program from Pragmatic AI Labs 01:30 - Democratizing access to two master's degrees worth of content 02:15 - Host's background as an adjunct professor at multiple universities 03:00 - The need for lifelong learning and universities' slow adaptation 04:10 - The freemium model of DS500: free courses on edX and community building 05:00 - Overview of current DS500 programs: LLM operations, Rust, MLOps, generative AI 06:45 - Upcoming programs in cloud computing and data engineering 07:30 - Partnering with experts and transparent profit-sharing model 08:20 - Introducing the founders: Noah Gift and Alfredo Deza 09:30 - Challenges in the online learning space: the medieval model of universities 11:00 - Opportunities for continuous access to material and price tiering 12:20 - UC Davis' innovative partnership with edX for alumni access 13:40 - The challenge of rapid innovation in universities vs. industry 15:00 - Pragmatic AI Labs' ability to create content at scale 16:00 - Targeting pre and post-master's degree students with an audit model 17:20 - The long-term vision of DS500 and the importance of continuous improvement 18:30 - Inviting listeners to join the DS500 journey and participate in courses 19:30 - Conclusion: the exciting launch of DS500 after eight years in the making
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
- 📚edX Professional Certificate Machine Learning Operations (MLOps): insight.paiml.com/ear
- 📚edX Professional Certificate Python Fundamentals for MLOps: https://insight.paiml.com/h5h
- 📚edX Professional Certificate DevOps, DataOps, MLOps: https://insight.paiml.com/mgk
- 📚edX Professional Certificate MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/rqv
- 📚edX Professional Certificate MLOps Platforms: Amazon SageMaker and Azure ML : https://insight.paiml.com/9mg
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
- 📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
00:00 - Introduction: Is 2024 the year of the dumb phone?
01:23 - Host's background and relationship with media consumption 03:15 - How smartphones are like having a TV in your pocket 04:50 - The limited benefits of smartphones (touchless pay, navigation) 06:10 - Considering the trade-offs: wasting hours per day scrolling 07:45 - Participating in the dumb phone revolution 09:02 - Valuing your time as a creator vs. being a consumer 10:15 - Concerns about tech giants partnering and privacy issues 11:40 - Key factors driving the dumb phone revolution 13:20 - Viewing smartphones as a failed hypothesis 14:35 - Closing analogy: smartphones as bad prototype code that needs deleting 16:00 - Encouragement to research dumb phones and concluding thoughts
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
Noah Gift reacts to Apple's announced partnership with OpenAI, arguing it abandons Apple's core values and poses risks to user privacy and creator livelihoods. He shares his personal plan and advice for gradually transitioning away from the Apple ecosystem to open-source alternatives like Ubuntu.
Add a full description of what happened in this episode, including topics discussed, useful timestamps, useful episode notes and any additional links you may want to share: As a 40-year Apple user, AI expert, and university professor, Noah Gift expresses deep concerns over Apple's partnership with OpenAI and outlines a path for leaving the Apple ecosystem:
- Despite marketing itself as a premium, pro-privacy, pro-creator brand, Apple partnering with OpenAI abandons those core values (2:00)
- OpenAI's leadership has a dubious track record, engages in regulatory entrepreneurship to exploit legal gray areas around data usage and fair use (6:00)
- Technical risks: OS-level AI integration shows lack of skill; possibility of OpenAI accessing sensitive iCloud creator data (9:00)
- Noah's step-by-step plan: 1) Ubuntu laptops like Framework or System76 2) Ditch Apple Watch for Garmin 3) Sell Mac Studio for AMD Threadripper (12:00)
- Opportunity to save money, reclaim time/attention from smartphone addiction, view computer as a tool vs. part of identity (14:30)
- Upcoming Pragmatic AI Labs course teaching artists to switch from Mac to open-source Ubuntu (17:00)
Consider exploring alternatives to Apple:
- Framework laptop: https://frame.work/
- System76 computers: https://system76.com/
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
Noah Gift, founder of Pragmatic AI Labs, shares his personal journey from working in TV/film to building an alternative university that combines the best of academia, content creation, and industry experience to provide cutting-edge AI/ML education accessible to all.
Add a full description of what happened in this episode, including topics discussed, useful timestamps, useful episode notes and any additional links you may want to share: Noah Gift traces his path to founding Pragmatic AI Labs, an alternative learning platform for AI/ML skills. Key points:
- Gained early experience in TV/film, IT, and visual effects at major studios like ABC, Disney, Sony (1:00)
- Transitioned to startups, consulting, and teaching machine learning which inspired the vision for Pragmatic AI Labs (5:30)
- Draws upon 8 years teaching graduate-level data science/AI at top universities like UC Berkeley, Duke, Northwestern (7:00)
- Extensive content creation with O'Reilly, Pearson, Udacity, edX, Coursera - 40 courses equivalent to 2 master's degrees (9:00)
- Pragmatic AI Labs uniquely combines strengths of universities (elite research), content companies (speed, quality), and industry (real-world relevance) (12:00)
- Currently offers 40 cutting-edge courses on edX spanning Python, Rust, data engineering, MLOps, generative AI, LLMs (14:00)
- Upcoming plans: onboarding top authors, growing Discord community, investing in podcasts (18:00)
- Transitioning to a transparent public benefit corporation to support authors, offer free learning pathways, further the mission of expanding access to AI/ML education (19:00)
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
- 📚edX Professional Certificate Machine Learning Operations (MLOps): insight.paiml.com/ear
- 📚edX Professional Certificate Python Fundamentals for MLOps: https://insight.paiml.com/h5h
- 📚edX Professional Certificate DevOps, DataOps, MLOps: https://insight.paiml.com/mgk
- 📚edX Professional Certificate MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/rqv
- 📚edX Professional Certificate MLOps Platforms: Amazon SageMaker and Azure ML : https://insight.paiml.com/9mg
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
- 📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
🎓📚 Unlock the power of AI with two Master's degrees worth of courses on edX, covering everything from ☁️ Cloud Computing to 🦀 Rust to 🤖 LLMs and 🎨 Generative AI! 🚀
👉 Join the Pragmatic AI Labs Community now:
- 🔥 edX 🔥
- 💬 Discord Community 💬
- 🌟 Coursera 🌟
🎉 Start your AI journey today and take your skills to the next level! 🎉
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
- 📚edX Professional Certificate Machine Learning Operations (MLOps): insight.paiml.com/ear
- 📚edX Professional Certificate Python Fundamentals for MLOps: https://insight.paiml.com/h5h
- 📚edX Professional Certificate DevOps, DataOps, MLOps: https://insight.paiml.com/mgk
- 📚edX Professional Certificate MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/rqv
- 📚edX Professional Certificate MLOps Platforms: Amazon SageMaker and Azure ML : https://insight.paiml.com/9mg
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
- 📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
- 📚edX Professional Certificate Machine Learning Operations (MLOps): insight.paiml.com/ear
- 📚edX Professional Certificate Python Fundamentals for MLOps: https://insight.paiml.com/h5h
- 📚edX Professional Certificate DevOps, DataOps, MLOps: https://insight.paiml.com/mgk
- 📚edX Professional Certificate MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/rqv
- 📚edX Professional Certificate MLOps Platforms: Amazon SageMaker and Azure ML : https://insight.paiml.com/9mg
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
- 📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
- 📚edX Professional Certificate Machine Learning Operations (MLOps): insight.paiml.com/ear
- 📚edX Professional Certificate Python Fundamentals for MLOps: https://insight.paiml.com/h5h
- 📚edX Professional Certificate DevOps, DataOps, MLOps: https://insight.paiml.com/mgk
- 📚edX Professional Certificate MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/rqv
- 📚edX Professional Certificate MLOps Platforms: Amazon SageMaker and Azure ML : https://insight.paiml.com/9mg
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
- 📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
- 📚edX Professional Certificate Machine Learning Operations (MLOps): insight.paiml.com/ear
- 📚edX Professional Certificate Python Fundamentals for MLOps: https://insight.paiml.com/h5h
- 📚edX Professional Certificate DevOps, DataOps, MLOps: https://insight.paiml.com/mgk
- 📚edX Professional Certificate MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/rqv
- 📚edX Professional Certificate MLOps Platforms: Amazon SageMaker and Azure ML : https://insight.paiml.com/9mg
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
- 📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
AWS Virtual Private Cloud (VPC) enables the creation of logically isolated virtual networks on the AWS Cloud, offering security, flexibility, and integration with various AWS services. CloudFront, a global content delivery network (CDN), ensures low latency, high data transfer speeds, and cost-effectiveness for content delivery.
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
📝 Rust Axum Microservice: https://insight.paiml.com/n9j
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
- 📚edX Professional Certificate Machine Learning Operations (MLOps): insight.paiml.com/ear
- 📚edX Professional Certificate Python Fundamentals for MLOps: https://insight.paiml.com/h5h
- 📚edX Professional Certificate DevOps, DataOps, MLOps: https://insight.paiml.com/mgk
- 📚edX Professional Certificate MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/rqv
- 📚edX Professional Certificate MLOps Platforms: Amazon SageMaker and Azure ML : https://insight.paiml.com/9mg
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
- 📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
📝 Rust Axum Microservice: https://insight.paiml.com/n9j
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
- 📚edX Professional Certificate Machine Learning Operations (MLOps): insight.paiml.com/ear
- 📚edX Professional Certificate Python Fundamentals for MLOps: https://insight.paiml.com/h5h
- 📚edX Professional Certificate DevOps, DataOps, MLOps: https://insight.paiml.com/mgk
- 📚edX Professional Certificate MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/rqv
- 📚edX Professional Certificate MLOps Platforms: Amazon SageMaker and Azure ML : https://insight.paiml.com/9mg
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
- 📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
- 📚edX Professional Certificate Machine Learning Operations (MLOps): insight.paiml.com/ear
- 📚edX Professional Certificate Python Fundamentals for MLOps: https://insight.paiml.com/h5h
- 📚edX Professional Certificate DevOps, DataOps, MLOps: https://insight.paiml.com/mgk
- 📚edX Professional Certificate MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/rqv
- 📚edX Professional Certificate MLOps Platforms: Amazon SageMaker and Azure ML : https://insight.paiml.com/9mg
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
- 📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
📝 Rust Axum Microservice: https://insight.paiml.com/n9j
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
- 📚edX Professional Certificate Machine Learning Operations (MLOps): insight.paiml.com/ear
- 📚edX Professional Certificate Python Fundamentals for MLOps: https://insight.paiml.com/h5h
- 📚edX Professional Certificate DevOps, DataOps, MLOps: https://insight.paiml.com/mgk
- 📚edX Professional Certificate MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/rqv
- 📚edX Professional Certificate MLOps Platforms: Amazon SageMaker and Azure ML : https://insight.paiml.com/9mg
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
- 📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
edX
✨I build courses: https://insight.paiml.com/d69
- 📚edX Professional Certificate in Rust Programming: https://insight.paiml.com/tkg
- 📚edX Rust Data Engineering: https://insight.paiml.com/fhd
- 📚edX Professional Certificate in Large Language Model Operations (LLMOps): https://insight.paiml.com/j8t
Coursera
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
- 📝 Rust Axum Microservice: https://insight.paiml.com/n9j
📝Local LLMs with Llamafile: https://insight.paiml.com/rw1
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
📝 Rust Axum Microservice: https://insight.paiml.com/n9j
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📝 Guided Projects:
- 📝Object-Oriented Programming in Python:https://insight.paiml.com/n4h
- 📝MySQL-for-Data-Engineering: https://insight.paiml.com/e1k
- 📝Python Generators: https://insight.paiml.com/i9l
- 📝Build a Static Website with Rust and Zola: https://insight.paiml.com/a2h
- 📝Building Rust AWS Lambda Microservices with Cargo Lambda: https://insight.paiml.com/8ed
- 📝Rust Secret Cipher CLI: https://insight.paiml.com/zzr
- 📝Python Decorators: https://insight.paiml.com/1n5
- 📝Bash Command-line tools: insight.paiml.com/zo3
- 📝Big O Notation in Python: https://insight.paiml.com/bnv
📝 Rust Axum Microservice: https://insight.paiml.com/n9j
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization: https://insight.paiml.com/a8e
- 📚Introduction to Generative AI: https://insight.paiml.com/ee2
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Advanced Data Engineering: https://insight.paiml.com/uvi
- 📚GenAI and LLMs on AWS: https://insight.paiml.com/3x7
- 📚Open Source LLMOps Solutions: https://insight.paiml.com/x0g
- 📚Foundations of Local Large Language models: https://insight.paiml.com/rvy
- 📚Beginning Llamafile for Local Large Language Models (LLMs): https://insight.paiml.com/5ec
- 📚End to End LLMs with Azure: https://coursera.org/learn/azure-llm-large-language-models
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
📚Cloud Virtualization, Containers and APIs:
https://insight.paiml.com/ce5
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot * Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering * Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke * MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke * Data Visualization with Python: https://insight.paiml.com/y9p * Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or * Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke * Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j * Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke * Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke * MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/ohq * Python Essentials for MLOps: https://insight.paiml.com/uvm * DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke * Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke * MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke * Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke * Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke * Cloud Computing Foundations: https://insight.paiml.com/zrb * Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot * Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering * Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke * MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke * Data Visualization with Python: https://insight.paiml.com/y9p * Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or * Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke * Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j * Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke * Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke * MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/ohq * Python Essentials for MLOps: https://insight.paiml.com/uvm * DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke * Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke * MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke * Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke * Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke * Cloud Computing Foundations: https://insight.paiml.com/zrb * Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization:
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Databricks to Local LLMs: https://insight.paiml.com/i6k
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
📚 Coursera Guided Projects:
- Object-Oriented Programming in Python:https://insight.paiml.com/n4h
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot * Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering * Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke * MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke * Data Visualization with Python: https://insight.paiml.com/y9p * Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or * Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke * Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j * Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke * Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke * MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/ohq * Python Essentials for MLOps: https://insight.paiml.com/uvm * DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke * Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke * MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke * Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke * Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke * Cloud Computing Foundations: https://insight.paiml.com/zrb * Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization:
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
- 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh
- 📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot * Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering * Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke * MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke * Data Visualization with Python: https://insight.paiml.com/y9p * Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or * Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke * Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j * Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke * Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke * MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/ohq * Python Essentials for MLOps: https://insight.paiml.com/uvm * DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke * Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke * MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke * Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke * Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke * Cloud Computing Foundations: https://insight.paiml.com/zrb * Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
✨I build courses: https://insight.paiml.com/bzf
- 📚LLMOps Specialization:
- 📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u
- 📚Rust Programming Specialization: https://insight.paiml.com/qwh
- 📚Rust for DevOps: https://insight.paiml.com/x14
- 📚Rust LLMOps: https://insight.paiml.com/g3b
- 📚Rust Fundamentals: https://insight.paiml.com/qyt
- 📚Data Engineering with Rust: https://insight.paiml.com/zm1
- 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
- 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9
- 📚Data Visualization with Python: https://insight.paiml.com/y9p
- 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp
- 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j
- 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u
- 📚Python Essentials for MLOps: https://insight.paiml.com/uvm
- 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi
- 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v
- 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb
- 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or
- 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31
- 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b
- 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7
- 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86
- 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
- 📚Cloud Computing Foundations: https://insight.paiml.com/zrb
- 📚Cloud Data Engineering: https://insight.paiml.com/75t
📚Cloud Machine Learning Engineering and MLOps:
https://insight.paiml.com/jjh
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
✨I build courses: https://insight.paiml.com/bzf
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* 📚Rust Programming Specialization: https://insight.paiml.com/qwh * 📚Rust for DevOps: https://insight.paiml.com/x14 * 📚Rust LLMOps: https://insight.paiml.com/g3b * 📚Rust Fundamentals: https://insight.paiml.com/qyt * 📚Data Engineering with Rust: https://insight.paiml.com/zm1 * 📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot * 📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9 * 📚Data Visualization with Python: https://insight.paiml.com/y9p * 📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp * 📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j * 📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u * 📚Python Essentials for MLOps: https://insight.paiml.com/uvm * 📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi * 📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v * 📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb * 📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or * 📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31 * 📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b * 📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7 * 📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86 * 📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt * 📚Cloud Computing Foundations: https://insight.paiml.com/zrb * 📚Cloud Data Engineering: https://insight.paiml.com/75t * 📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh * 📚Cloud Data Engineering: https://insight.paiml.com/0y3
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot * Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering * Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke * MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke * Data Visualization with Python: https://insight.paiml.com/y9p * Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or * Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke * Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j * Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke * Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke * MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/ohq * Python Essentials for MLOps: https://insight.paiml.com/uvm * DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke * Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke * MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke * Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke * Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke * Cloud Computing Foundations: https://insight.paiml.com/zrb * Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot * Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering * Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke * MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke * Data Visualization with Python: https://insight.paiml.com/y9p * Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or * Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke * Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j * Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke * Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke * MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/ohq * Python Essentials for MLOps: https://insight.paiml.com/uvm * DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke * Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke * MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke * Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke * Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke * Cloud Computing Foundations: https://insight.paiml.com/zrb * Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot * Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering * Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke * MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke * Data Visualization with Python: https://insight.paiml.com/y9p * Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or * Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke * Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j * Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke * Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke * MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/ohq * Python Essentials for MLOps: https://insight.paiml.com/uvm * DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke * Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke * MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke * Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke * Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke * Cloud Computing Foundations: https://insight.paiml.com/zrb * Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot * Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering * Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke * MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke * Data Visualization with Python: https://insight.paiml.com/y9p * Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or * Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke * Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j * Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke * Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke * MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/ohq * Python Essentials for MLOps: https://insight.paiml.com/uvm * DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke * Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke * MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke * Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke * Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke * Cloud Computing Foundations: https://insight.paiml.com/zrb * Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://insight.paiml.com/qwh * Rust for DevOps: https://insight.paiml.com/x14 * Rust LLMOps: https://insight.paiml.com/g3b * Rust Fundamentals: https://insight.paiml.com/qyt * Data Engineering with Rust: https://insight.paiml.com/zm1 * Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://www.coursera.org/specializations/rust-programming * Rust for DevOps: https://www.coursera.org/learn/rust-for-devops?specialization=rust-programming * Rust LLMOps: https://www.coursera.org/learn/rust-llmops?specialization=rust-programming * Rust Fundamentals: https://www.coursera.org/learn/rust-fundamentals * Data Engineering with Rust: https://www.coursera.org/programs/duke-university-on-coursera-obsio/learn/data-engineering-rust * Python and Rust with Linux Command Line Tools: https://www.coursera.org/learn/python-rust-linux * Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering * Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke * MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke * Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python * Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke * Spark, Hadoop, and Snowflake for Data Engineering: https://www.coursera.org/learn/spark-hadoop-snowflake-data-engineering * Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke * Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke * Python Essentials for MLOps: https://www.coursera.org/learn/python-mlops-duke * DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke * Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke * MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke * Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke * Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke * Cloud Computing Foundations: https://www.coursera.org/learn/cloud-computing-foundations-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀* Rust Programming Specialization: https://www.coursera.org/specializations/rust-programming * Rust for DevOps: https://www.coursera.org/learn/rust-for-devops?specialization=rust-programming * Rust LLMOps: https://www.coursera.org/learn/rust-llmops?specialization=rust-programming * Rust Fundamentals: https://www.coursera.org/learn/rust-fundamentals * Data Engineering with Rust: https://www.coursera.org/programs/duke-university-on-coursera-obsio/learn/data-engineering-rust * Python and Rust with Linux Command Line Tools: https://www.coursera.org/learn/python-rust-linux * Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering * Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke * MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke * Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python * Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke * Spark, Hadoop, and Snowflake for Data Engineering: https://www.coursera.org/learn/spark-hadoop-snowflake-data-engineering * Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke * Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke * Python Essentials for MLOps: https://www.coursera.org/learn/python-mlops-duke * DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke * Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke * MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke * Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke * Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke * Cloud Computing Foundations: https://www.coursera.org/learn/cloud-computing-foundations-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀
Rust Fundamentals: https://www.coursera.org/learn/rust-fundamentals
Data Engineering with Rust: https://www.coursera.org/programs/duke-university-on-coursera-obsio/learn/data-engineering-rust
Python and Rust with Linux Command Line Tools: https://www.coursera.org/learn/python-rust-linux
Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering
Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke
MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke
Spark, Hadoop, and Snowflake for Data Engineering: https://www.coursera.org/learn/spark-hadoop-snowflake-data-engineering
Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke
Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke
Python Essentials for MLOps: https://www.coursera.org/learn/python-mlops-duke
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke
MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke
Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke
Cloud Computing Foundations: https://www.coursera.org/learn/cloud-computing-foundations-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
If you enjoyed this content, your learning journey has just begun! Dive deeper into the fascinating world of technology with these hand-picked resources:
📊 Data Visualization and Python:
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
🛠️ DevOps, MLOps, and Cloud Computing:
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
🐍 Python Essentials:
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
If you enjoyed this video, your learning journey has just begun! Dive deeper into the fascinating world of technology with these hand-picked resources:
📊 Data Visualization and Python:
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
🛠️ DevOps, MLOps, and Cloud Computing:
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
🐍 Python Essentials:
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this post, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀
Rust Fundamentals: https://www.coursera.org/learn/rust-fundamentals
Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering
Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke
MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke
Spark, Hadoop, and Snowflake for Data Engineering: https://www.coursera.org/learn/spark-hadoop-snowflake-data-engineering
Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke
Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke
Python Essentials for MLOps: https://www.coursera.org/learn/python-mlops-duke
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke
MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke
Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke
Cloud Computing Foundations: https://www.coursera.org/learn/cloud-computing-foundations-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
Hey readers 👋, if you enjoyed this post, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Data, Cloud, and AI 🚀
Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke
MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke
Spark, Hadoop, and Snowflake for Data Engineering: https://www.coursera.org/learn/spark-hadoop-snowflake-data-engineering
Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke
Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke
Python Essentials for MLOps: https://www.coursera.org/learn/python-mlops-duke
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke
MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke
Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke
Cloud Computing Foundations: https://www.coursera.org/learn/cloud-computing-foundations-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
If you enjoyed this content, your learning journey has just begun! Dive deeper into the fascinating world of technology with these hand-picked resources:
📊 Data Visualization and Python:
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
🛠️ DevOps, MLOps, and Cloud Computing:
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
🐍 Python Essentials:
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
If you enjoyed this content, your learning journey has just begun! Dive deeper into the fascinating world of technology with these hand-picked resources:
📊 Data Visualization and Python:
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
🛠️ DevOps, MLOps, and Cloud Computing:
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
🐍 Python Essentials:
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
If you enjoyed this content, your learning journey has just begun! Dive deeper into the fascinating world of technology with these hand-picked resources:
📊 Data Visualization and Python:
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
🛠️ DevOps, MLOps, and Cloud Computing:
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
🐍 Python Essentials:
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.
You can also find many courses and programs on edX here:
I build courses: Pragmatic AI Labs on edX
If you enjoyed this video, your learning journey has just begun! Dive deeper into the fascinating world of technology with these hand-picked resources:
📊 Data Visualization and Python:
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
🛠️ DevOps, MLOps, and Cloud Computing:
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
🐍 Python Essentials:
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
If you enjoyed this video, your learning journey has just begun! Dive deeper into the fascinating world of technology with these hand-picked resources:
📊 Data Visualization and Python:
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
🛠️ DevOps, MLOps, and Cloud Computing:
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
🐍 Python Essentials:
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, your learning journey has just begun! Dive deeper into the fascinating world of technology with these hand-picked resources:
📊 Data Visualization and Python:
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
🛠️ DevOps, MLOps, and Cloud Computing:
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
🐍 Python Essentials:
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
🎥 Follow & Subscribe:
Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
Your adventure in tech awaits! Dive in now, and elevate your skills to new heights. 🚀
If you enjoyed this video, here are additional resources to look at:
MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Open Source Platforms for MLOps: https://www.coursera.org/learn/open-source-mlops-platforms-duke
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Buy book here: https://www.amazon.com/The-Enterprise-Data-Catalog/dp/149209871X
Open Source Platforms for MLOps: https://www.coursera.org/learn/open-source-mlops-platforms-duke
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Open Source Platforms for MLOps: https://www.coursera.org/learn/open-source-mlops-platforms-duke
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Open Source Platforms for MLOps: https://www.coursera.org/learn/open-source-mlops-platforms-duke
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Chat with Maxime David. We discuss Rust and how it makes for an ideal language for AWS Lambda. Checkout his Rust YouTube Channel here: @maxday_coding and his lambda-perf repo here: https://github.com/maxday/lambda-perf
If you enjoyed this video, here are additional resources to look at:
Open Source Platforms for MLOps: https://www.coursera.org/learn/open-source-mlops-platforms-duke
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
[00:00.000 --> 00:04.560] All right, so I'm here with 52 weeks of AWS
[00:04.560 --> 00:07.920] and still continuing to do developer certification.
[00:07.920 --> 00:11.280] I'm gonna go ahead and share my screen here.
[00:13.720 --> 00:18.720] All right, so we are on Lambda, one of my favorite topics.
[00:19.200 --> 00:20.800] Let's get right into it
[00:20.800 --> 00:24.040] and talk about how to develop event-driven solutions
[00:24.040 --> 00:25.560] with AWS Lambda.
[00:26.640 --> 00:29.440] With Serverless Computing, one of the things
[00:29.440 --> 00:32.920] that it is going to do is it's gonna change
[00:32.920 --> 00:36.000] the way you think about building software
[00:36.000 --> 00:39.000] and in a traditional deployment environment,
[00:39.000 --> 00:42.040] you would configure an instance, you would update an OS,
[00:42.040 --> 00:45.520] you'd install applications, build and deploy them,
[00:45.520 --> 00:47.000] load balance.
[00:47.000 --> 00:51.400] So this is non-cloud native computing and Serverless,
[00:51.400 --> 00:54.040] you really only need to focus on building
[00:54.040 --> 00:56.360] and deploying applications and then monitoring
[00:56.360 --> 00:58.240] and maintaining the applications.
[00:58.240 --> 01:00.680] And so with really what Serverless does
[01:00.680 --> 01:05.680] is it allows you to focus on the code for the application
[01:06.320 --> 01:08.000] and you don't have to manage the operating system,
[01:08.000 --> 01:12.160] the servers or scale it and really is a huge advantage
[01:12.160 --> 01:14.920] because you don't have to pay for the infrastructure
[01:14.920 --> 01:15.920] when the code isn't running.
[01:15.920 --> 01:18.040] And that's really a key takeaway.
[01:19.080 --> 01:22.760] If you take a look at the AWS Serverless platform,
[01:22.760 --> 01:24.840] there's a bunch of fully managed services
[01:24.840 --> 01:26.800] that are tightly integrated with Lambda.
[01:26.800 --> 01:28.880] And so this is another huge advantage of Lambda,
[01:28.880 --> 01:31.000] isn't necessarily that it's the fastest
[01:31.000 --> 01:33.640] or it has the most powerful execution,
[01:33.640 --> 01:35.680] it's the tight integration with the rest
[01:35.680 --> 01:39.320] of the AWS platform and developer tools
[01:39.320 --> 01:43.400] like AWS Serverless application model or AWS SAM
[01:43.400 --> 01:45.440] would help you simplify the deployment
[01:45.440 --> 01:47.520] of Serverless applications.
[01:47.520 --> 01:51.960] And some of the services include Amazon S3,
[01:51.960 --> 01:56.960] Amazon SNS, Amazon SQS and AWS SDKs.
[01:58.600 --> 02:03.280] So in terms of Lambda, AWS Lambda is a compute service
[02:03.280 --> 02:05.680] for Serverless and it lets you run code
[02:05.680 --> 02:08.360] without provisioning or managing servers.
[02:08.360 --> 02:11.640] It allows you to trigger your code in response to events
[02:11.640 --> 02:14.840] that you would configure like, for example,
[02:14.840 --> 02:19.200] dropping something into a S3 bucket like that's an image,
[02:19.200 --> 02:22.200] Nevel Lambda that transcribes it to a different format.
[02:23.080 --> 02:27.200] It also allows you to scale automatically based on demand
[02:27.200 --> 02:29.880] and it will also incorporate built-in monitoring
[02:29.880 --> 02:32.880] and logging with AWS CloudWatch.
[02:34.640 --> 02:37.200] So if you look at AWS Lambda,
[02:37.200 --> 02:39.040] some of the things that it does
[02:39.040 --> 02:42.600] is it enables you to bring in your own code.
[02:42.600 --> 02:45.280] So the code you write for Lambda isn't written
[02:45.280 --> 02:49.560] in a new language, you can write things
[02:49.560 --> 02:52.600] in tons of different languages for AWS Lambda,
[02:52.600 --> 02:57.600] Node, Java, Python, C-sharp, Go, Ruby.
[02:57.880 --> 02:59.440] There's also custom run time.
[02:59.440 --> 03:03.880] So you could do Rust or Swift or something like that.
[03:03.880 --> 03:06.080] And it also integrates very deeply
[03:06.080 --> 03:11.200] with other AWS services and you can invoke
[03:11.200 --> 03:13.360] third-party applications as well.
[03:13.360 --> 03:18.080] It also has a very flexible resource and concurrency model.
[03:18.080 --> 03:20.600] And so Lambda would scale in response to events.
[03:20.600 --> 03:22.880] So you would just need to configure memory settings
[03:22.880 --> 03:24.960] and AWS would handle the other details
[03:24.960 --> 03:28.720] like the CPU, the network, the IO throughput.
[03:28.720 --> 03:31.400] Also, you can use the Lambda,
[03:31.400 --> 03:35.000] AWS Identity and Access Management Service or IAM
[03:35.000 --> 03:38.560] to grant access to what other resources you would need.
[03:38.560 --> 03:41.200] And this is one of the ways that you would control
[03:41.200 --> 03:44.720] the security of Lambda is you have really guardrails
[03:44.720 --> 03:47.000] around it because you would just tell Lambda,
[03:47.000 --> 03:50.080] you have a role that is whatever it is you need Lambda to do,
[03:50.080 --> 03:52.200] talk to SQS or talk to S3,
[03:52.200 --> 03:55.240] and it would specifically only do that role.
[03:55.240 --> 04:00.240] And the other thing about Lambda is that it has built-in
[04:00.560 --> 04:02.360] availability and fault tolerance.
[04:02.360 --> 04:04.440] So again, it's a fully managed service,
[04:04.440 --> 04:07.520] it's high availability and you don't have to do anything
[04:07.520 --> 04:08.920] at all to use that.
[04:08.920 --> 04:11.600] And one of the biggest things about Lambda
[04:11.600 --> 04:15.000] is that you only pay for what you use.
[04:15.000 --> 04:18.120] And so when the Lambda service is idle,
[04:18.120 --> 04:19.480] you don't have to actually pay for that
[04:19.480 --> 04:21.440] versus if it's something else,
[04:21.440 --> 04:25.240] like even in the case of a Kubernetes-based system,
[04:25.240 --> 04:28.920] still there's a host machine that's running Kubernetes
[04:28.920 --> 04:31.640] and you have to actually pay for that.
[04:31.640 --> 04:34.520] So one of the ways that you can think about Lambda
[04:34.520 --> 04:38.040] is that there's a bunch of different use cases for it.
[04:38.040 --> 04:40.560] So let's start off with different use cases,
[04:40.560 --> 04:42.920] web apps, I think would be one of the better ones
[04:42.920 --> 04:43.880] to think about.
[04:43.880 --> 04:46.680] So you can combine AWS Lambda with other services
[04:46.680 --> 04:49.000] and you can build powerful web apps
[04:49.000 --> 04:51.520] that automatically scale up and down.
[04:51.520 --> 04:54.000] And there's no administrative effort at all.
[04:54.000 --> 04:55.160] There's no backups necessary,
[04:55.160 --> 04:58.320] no multi-data center redundancy, it's done for you.
[04:58.320 --> 05:01.400] Backends, so you can build serverless backends
[05:01.400 --> 05:05.680] that lets you handle web, mobile, IoT,
[05:05.680 --> 05:07.760] third-party applications.
[05:07.760 --> 05:10.600] You can also build those backends with Lambda,
[05:10.600 --> 05:15.400] with API Gateway, and you can build applications with them.
[05:15.400 --> 05:17.200] In terms of data processing,
[05:17.200 --> 05:19.840] you can also use Lambda to run code
[05:19.840 --> 05:22.560] in response to a trigger, change in data,
[05:22.560 --> 05:24.440] shift in system state,
[05:24.440 --> 05:27.360] and really all of AWS for the most part
[05:27.360 --> 05:29.280] is able to be orchestrated with Lambda.
[05:29.280 --> 05:31.800] So it's really like a glue type service
[05:31.800 --> 05:32.840] that you're able to use.
[05:32.840 --> 05:36.600] Now chatbots, that's another great use case for it.
[05:36.600 --> 05:40.760] Amazon Lex is a service for building conversational chatbots
[05:42.120 --> 05:43.560] and you could use it with Lambda.
[05:43.560 --> 05:48.560] Amazon Lambda service is also able to be used
[05:50.080 --> 05:52.840] with voice IT automation.
[05:52.840 --> 05:55.760] These are all great use cases for Lambda.
[05:55.760 --> 05:57.680] In fact, I would say it's kind of like
[05:57.680 --> 06:01.160] the go-to automation tool for AWS.
[06:01.160 --> 06:04.160] So let's talk about how Lambda works next.
[06:04.160 --> 06:06.080] So the way Lambda works is that
[06:06.080 --> 06:09.080] there's a function and there's an event source,
[06:09.080 --> 06:10.920] and these are the core components.
[06:10.920 --> 06:14.200] The event source is the entity that publishes events
[06:14.200 --> 06:19.000] to AWS Lambda, and Lambda function is the code
[06:19.000 --> 06:21.960] that you're gonna use to process the event.
[06:21.960 --> 06:25.400] And AWS Lambda would run that Lambda function
[06:25.400 --> 06:29.600] on your behalf, and a few things to consider
[06:29.600 --> 06:33.840] is that it really is just a little bit of code,
[06:33.840 --> 06:35.160] and you can configure the triggers
[06:35.160 --> 06:39.720] to invoke a function in response to resource lifecycle events,
[06:39.720 --> 06:43.680] like for example, responding to incoming HTTP,
[06:43.680 --> 06:47.080] consuming events from a queue, like in the case of SQS
[06:47.080 --> 06:48.320] or running it on a schedule.
[06:48.320 --> 06:49.760] So running it on a schedule is actually
[06:49.760 --> 06:51.480] a really good data engineering task, right?
[06:51.480 --> 06:54.160] Like you could run it periodically to scrape a website.
[06:55.120 --> 06:58.080] So as a developer, when you create Lambda functions
[06:58.080 --> 07:01.400] that are managed by the AWS Lambda service,
[07:01.400 --> 07:03.680] you can define the permissions for the function
[07:03.680 --> 07:06.560] and basically specify what are the events
[07:06.560 --> 07:08.520] that would actually trigger it.
[07:08.520 --> 07:11.000] You can also create a deployment package
[07:11.000 --> 07:12.920] that includes application code
[07:12.920 --> 07:17.000] in any dependency or library necessary to run the code,
[07:17.000 --> 07:19.200] and you can also configure things like the memory,
[07:19.200 --> 07:23.200] you can figure the timeout, also configure the concurrency,
[07:23.200 --> 07:25.160] and then when your function is invoked,
[07:25.160 --> 07:27.640] Lambda will provide a runtime environment
[07:27.640 --> 07:30.080] based on the runtime and configuration options
[07:30.080 --> 07:31.080] that you selected.
[07:31.080 --> 07:36.080] So let's talk about models for invoking Lambda functions.
[07:36.360 --> 07:41.360] In the case of an event source that invokes Lambda function
[07:41.440 --> 07:43.640] by either a push or a pool model,
[07:43.640 --> 07:45.920] in the case of a push, it would be an event source
[07:45.920 --> 07:48.440] directly invoking the Lambda function
[07:48.440 --> 07:49.840] when the event occurs.
[07:50.720 --> 07:53.040] In the case of a pool model,
[07:53.040 --> 07:56.960] this would be putting the information into a stream or a queue,
[07:56.960 --> 07:59.400] and then Lambda would pull that stream or queue,
[07:59.400 --> 08:02.800] and then invoke the function when it detects an events.
[08:04.080 --> 08:06.480] So a few different examples would be
[08:06.480 --> 08:11.280] that some services can actually invoke the function directly.
[08:11.280 --> 08:13.680] So for a synchronous invocation,
[08:13.680 --> 08:15.480] the other service would wait for the response
[08:15.480 --> 08:16.320] from the function.
[08:16.320 --> 08:20.680] So a good example would be in the case of Amazon API Gateway,
[08:20.680 --> 08:24.800] which would be the REST-based service in front.
[08:24.800 --> 08:28.320] In this case, when a client makes a request to your API,
[08:28.320 --> 08:31.200] that client would get a response immediately.
[08:31.200 --> 08:32.320] And then with this model,
[08:32.320 --> 08:34.880] there's no built-in retry in Lambda.
[08:34.880 --> 08:38.040] Examples of this would be Elastic Load Balancing,
[08:38.040 --> 08:42.800] Amazon Cognito, Amazon Lex, Amazon Alexa,
[08:42.800 --> 08:46.360] Amazon API Gateway, AWS CloudFormation,
[08:46.360 --> 08:48.880] and Amazon CloudFront,
[08:48.880 --> 08:53.040] and also Amazon Kinesis Data Firehose.
[08:53.040 --> 08:56.760] For asynchronous invocation, AWS Lambda queues,
[08:56.760 --> 09:00.320] the event before it passes to your function.
[09:00.320 --> 09:02.760] The other service gets a success response
[09:02.760 --> 09:04.920] as soon as the event is queued,
[09:04.920 --> 09:06.560] and if an error occurs,
[09:06.560 --> 09:09.760] Lambda will automatically retry the invocation twice.
[09:10.760 --> 09:14.520] A good example of this would be S3, SNS,
[09:14.520 --> 09:17.720] SES, the Simple Email Service,
[09:17.720 --> 09:21.120] AWS CloudFormation, Amazon CloudWatch Logs,
[09:21.120 --> 09:25.400] CloudWatch Events, AWS CodeCommit, and AWS Config.
[09:25.400 --> 09:28.280] But in both cases, you can invoke a Lambda function
[09:28.280 --> 09:30.000] using the invoke operation,
[09:30.000 --> 09:32.720] and you can specify the invocation type
[09:32.720 --> 09:35.440] as either synchronous or asynchronous.
[09:35.440 --> 09:38.760] And when you use the AWS service as a trigger,
[09:38.760 --> 09:42.280] the invocation type is predetermined for each service,
[09:42.280 --> 09:44.920] and so you have no control over the invocation type
[09:44.920 --> 09:48.920] that these events sources use when they invoke your Lambda.
[09:50.800 --> 09:52.120] In the polling model,
[09:52.120 --> 09:55.720] the event sources will put information into a stream or a queue,
[09:55.720 --> 09:59.360] and AWS Lambda will pull the stream or the queue.
[09:59.360 --> 10:01.000] If it first finds a record,
[10:01.000 --> 10:03.280] it will deliver the payload and invoke the function.
[10:03.280 --> 10:04.920] And this model, the Lambda itself,
[10:04.920 --> 10:07.920] is basically pulling data from a stream or a queue
[10:07.920 --> 10:10.280] for processing by the Lambda function.
[10:10.280 --> 10:12.640] Some examples would be a stream-based event service
[10:12.640 --> 10:17.640] would be Amazon DynamoDB or Amazon Kinesis Data Streams,
[10:17.800 --> 10:20.920] and these stream records are organized into shards.
[10:20.920 --> 10:24.640] So Lambda would actually pull the stream for the record
[10:24.640 --> 10:27.120] and then attempt to invoke the function.
[10:27.120 --> 10:28.800] If there's a failure,
[10:28.800 --> 10:31.480] AWS Lambda won't read any of the new shards
[10:31.480 --> 10:34.840] until the failed batch of records expires or is processed
[10:34.840 --> 10:36.160] successfully.
[10:36.160 --> 10:39.840] In the non-streaming event, which would be SQS,
[10:39.840 --> 10:42.400] Amazon would pull the queue for records.
[10:42.400 --> 10:44.600] If it fails or times out,
[10:44.600 --> 10:46.640] then the message would be returned to the queue,
[10:46.640 --> 10:49.320] and then Lambda will keep retrying the failed message
[10:49.320 --> 10:51.800] until it's processed successfully.
[10:51.800 --> 10:53.600] If the message will expire,
[10:53.600 --> 10:56.440] which is something you can do with SQS,
[10:56.440 --> 10:58.240] then it'll just be discarded.
[10:58.240 --> 11:00.400] And you can create a mapping between an event source
[11:00.400 --> 11:02.960] and a Lambda function right inside of the console.
[11:02.960 --> 11:05.520] And this is how typically you would set that up manually
[11:05.520 --> 11:07.600] without using infrastructure as code.
[11:08.560 --> 11:10.200] All right, let's talk about permissions.
[11:10.200 --> 11:13.080] This is definitely an easy place to get tripped up
[11:13.080 --> 11:15.760] when you're first using AWS Lambda.
[11:15.760 --> 11:17.840] There's two types of permissions.
[11:17.840 --> 11:20.120] The first is the event source and permission
[11:20.120 --> 11:22.320] to trigger the Lambda function.
[11:22.320 --> 11:24.480] This would be the invocation permission.
[11:24.480 --> 11:26.440] And the next one would be the Lambda function
[11:26.440 --> 11:29.600] needs permissions to interact with other services,
[11:29.600 --> 11:31.280] but this would be the run permissions.
[11:31.280 --> 11:34.520] And these are both handled via the IAM service
[11:34.520 --> 11:38.120] or the AWS identity and access management service.
[11:38.120 --> 11:43.120] So the IAM resource policy would tell the Lambda service
[11:43.600 --> 11:46.640] which push event the sources have permission
[11:46.640 --> 11:48.560] to invoke the Lambda function.
[11:48.560 --> 11:51.120] And these resource policies would make it easy
[11:51.120 --> 11:55.280] to grant access to a Lambda function across AWS account.
[11:55.280 --> 11:58.400] So a good example would be if you have an S3 bucket
[11:58.400 --> 12:01.400] in your account and you need to invoke a function
[12:01.400 --> 12:03.880] in another account, you could create a resource policy
[12:03.880 --> 12:07.120] that allows those to interact with each other.
[12:07.120 --> 12:09.200] And the resource policy for a Lambda function
[12:09.200 --> 12:11.200] is called a function policy.
[12:11.200 --> 12:14.160] And when you add a trigger to your Lambda function
[12:14.160 --> 12:16.760] from the console, the function policy
[12:16.760 --> 12:18.680] will be generated automatically
[12:18.680 --> 12:20.040] and it allows the event source
[12:20.040 --> 12:22.820] to take the Lambda invoke function action.
[12:24.400 --> 12:27.320] So a good example would be in Amazon S3 permission
[12:27.320 --> 12:32.120] to invoke the Lambda function called my first function.
[12:32.120 --> 12:34.720] And basically it would be an effect allow.
[12:34.720 --> 12:36.880] And then under principle, if you would have service
[12:36.880 --> 12:41.880] S3.AmazonEWS.com, the action would be Lambda colon
[12:41.880 --> 12:45.400] invoke function and then the resource would be the name
[12:45.400 --> 12:49.120] or the ARN of actually the Lambda.
[12:49.120 --> 12:53.080] And then the condition would be actually the ARN of the bucket.
[12:54.400 --> 12:56.720] And really that's it in a nutshell.
[12:57.560 --> 13:01.480] The Lambda execution role grants your Lambda function
[13:01.480 --> 13:05.040] permission to access AWS services and resources.
[13:05.040 --> 13:08.000] And you select or create the execution role
[13:08.000 --> 13:10.000] when you create a Lambda function.
[13:10.000 --> 13:12.320] The IAM policy would define the actions
[13:12.320 --> 13:14.440] of Lambda functions allowed to take
[13:14.440 --> 13:16.720] and the trust policy allows the Lambda service
[13:16.720 --> 13:20.040] to assume an execution role.
[13:20.040 --> 13:23.800] To grant permissions to AWS Lambda to assume a role,
[13:23.800 --> 13:27.460] you have to have the permission for IAM pass role action.
[13:28.320 --> 13:31.000] A couple of different examples of a relevant policy
[13:31.000 --> 13:34.560] for an execution role and the example,
[13:34.560 --> 13:37.760] the IAM policy, you know,
[13:37.760 --> 13:39.840] basically that we talked about earlier,
[13:39.840 --> 13:43.000] would allow you to interact with S3.
[13:43.000 --> 13:45.360] Another example would be to make it interact
[13:45.360 --> 13:49.240] with CloudWatch logs and to create a log group
[13:49.240 --> 13:51.640] and stream those logs.
[13:51.640 --> 13:54.800] The trust policy would give Lambda service permissions
[13:54.800 --> 13:57.600] to assume a role and invoke a Lambda function
[13:57.600 --> 13:58.520] on your behalf.
[13:59.560 --> 14:02.600] Now let's talk about the overview of authoring
[14:02.600 --> 14:06.120] and configuring Lambda functions.
[14:06.120 --> 14:10.440] So really to start with, to create a Lambda function,
[14:10.440 --> 14:14.840] you first need to create a Lambda function deployment package,
[14:14.840 --> 14:19.800] which is a zip or jar file that consists of your code
[14:19.800 --> 14:23.160] and any dependencies with Lambda,
[14:23.160 --> 14:25.400] you can use the programming language
[14:25.400 --> 14:27.280] and integrated development environment
[14:27.280 --> 14:29.800] that you're most familiar with.
[14:29.800 --> 14:33.360] And you can actually bring the code you've already written.
[14:33.360 --> 14:35.960] And Lambda does support lots of different languages
[14:35.960 --> 14:39.520] like Node.js, Python, Ruby, Java, Go,
[14:39.520 --> 14:41.160] and.NET runtimes.
[14:41.160 --> 14:44.120] And you can also implement a custom runtime
[14:44.120 --> 14:45.960] if you wanna use a different language as well,
[14:45.960 --> 14:48.480] which is actually pretty cool.
[14:48.480 --> 14:50.960] And if you wanna create a Lambda function,
[14:50.960 --> 14:52.800] you would specify the handler,
[14:52.800 --> 14:55.760] the Lambda function handler is the entry point.
[14:55.760 --> 14:57.600] And a few different aspects of it
[14:57.600 --> 14:59.400] that are important to pay attention to,
[14:59.400 --> 15:00.720] the event object,
[15:00.720 --> 15:03.480] this would provide information about the event
[15:03.480 --> 15:05.520] that triggered the Lambda function.
[15:05.520 --> 15:08.280] And this could be like a predefined object
[15:08.280 --> 15:09.760] that AWS service generates.
[15:09.760 --> 15:11.520] So you'll see this, like for example,
[15:11.520 --> 15:13.440] in the console of AWS,
[15:13.440 --> 15:16.360] you can actually ask for these objects
[15:16.360 --> 15:19.200] and it'll give you really the JSON structure
[15:19.200 --> 15:20.680] so you can test things out.
[15:21.880 --> 15:23.900] In the contents of an event object
[15:23.900 --> 15:26.800] includes everything you would need to actually invoke it.
[15:26.800 --> 15:29.640] The context object is generated by AWS
[15:29.640 --> 15:32.360] and this is really a runtime information.
[15:32.360 --> 15:35.320] And so if you needed to get some kind of runtime information
[15:35.320 --> 15:36.160] about your code,
[15:36.160 --> 15:40.400] let's say environmental variables or AWS request ID
[15:40.400 --> 15:44.280] or a log stream or remaining time in Millies,
[15:45.320 --> 15:47.200] like for example, that one would return
[15:47.200 --> 15:48.840] the number of milliseconds that remain
[15:48.840 --> 15:50.600] before your function times out,
[15:50.600 --> 15:53.300] you can get all that inside the context object.
[15:54.520 --> 15:57.560] So what about an example that runs a Python?
[15:57.560 --> 15:59.280] Pretty straightforward actually.
[15:59.280 --> 16:01.400] All you need is you would put a handler
[16:01.400 --> 16:03.280] inside the handler would take,
[16:03.280 --> 16:05.000] that it would be a Python function,
[16:05.000 --> 16:07.080] it would be an event, there'd be a context,
[16:07.080 --> 16:10.960] you pass it inside and then you return some kind of message.
[16:10.960 --> 16:13.960] A few different best practices to remember
[16:13.960 --> 16:17.240] about AWS Lambda would be to separate
[16:17.240 --> 16:20.320] the core business logic from the handler method
[16:20.320 --> 16:22.320] and this would make your code more portable,
[16:22.320 --> 16:24.280] enable you to target unit tests
[16:25.240 --> 16:27.120] without having to worry about the configuration.
[16:27.120 --> 16:30.400] So this is always a really good idea just in general.
[16:30.400 --> 16:32.680] Make sure you have modular functions.
[16:32.680 --> 16:34.320] So you have a single purpose function,
[16:34.320 --> 16:37.160] you don't have like a kitchen sink function,
[16:37.160 --> 16:40.000] you treat functions as stateless as well.
[16:40.000 --> 16:42.800] So you would treat a function that basically
[16:42.800 --> 16:46.040] just does one thing and then when it's done,
[16:46.040 --> 16:48.320] there is no state that's actually kept anywhere
[16:49.320 --> 16:51.120] and also only include what you need.
[16:51.120 --> 16:55.840] So you don't want to have a huge sized Lambda functions
[16:55.840 --> 16:58.560] and one of the ways that you can avoid this
[16:58.560 --> 17:02.360] is by reducing the time it takes a Lambda to unpack
[17:02.360 --> 17:04.000] the deployment packages
[17:04.000 --> 17:06.600] and you can also minimize the complexity
[17:06.600 --> 17:08.640] of your dependencies as well.
[17:08.640 --> 17:13.600] And you can also reuse the temporary runtime environment
[17:13.600 --> 17:16.080] to improve the performance of a function as well.
[17:16.080 --> 17:17.680] And so the temporary runtime environment
[17:17.680 --> 17:22.280] initializes any external dependencies of the Lambda code
[17:22.280 --> 17:25.760] and you can make sure that any externalized configuration
[17:25.760 --> 17:27.920] or dependency that your code retrieves are stored
[17:27.920 --> 17:30.640] and referenced locally after the initial run.
[17:30.640 --> 17:33.800] So this would be limit re-initializing variables
[17:33.800 --> 17:35.960] and objects on every invocation,
[17:35.960 --> 17:38.200] keeping it alive and reusing connections
[17:38.200 --> 17:40.680] like an HTTP or database
[17:40.680 --> 17:43.160] that were established during the previous invocation.
[17:43.160 --> 17:45.880] So a really good example of this would be a socket connection.
[17:45.880 --> 17:48.040] If you make a socket connection
[17:48.040 --> 17:51.640] and this socket connection took two seconds to spawn,
[17:51.640 --> 17:54.000] you don't want every time you call Lambda
[17:54.000 --> 17:55.480] for it to wait two seconds,
[17:55.480 --> 17:58.160] you want to reuse that socket connection.
[17:58.160 --> 18:00.600] A few good examples of best practices
[18:00.600 --> 18:02.840] would be including logging statements.
[18:02.840 --> 18:05.480] This is a kind of a big one
[18:05.480 --> 18:08.120] in the case of any cloud computing operation,
[18:08.120 --> 18:10.960] especially when it's distributed, if you don't log it,
[18:10.960 --> 18:13.280] there's no way you can figure out what's going on.
[18:13.280 --> 18:16.560] So you must add logging statements that have context
[18:16.560 --> 18:19.720] so you know which particular Lambda instance
[18:19.720 --> 18:21.600] is actually occurring in.
[18:21.600 --> 18:23.440] Also include results.
[18:23.440 --> 18:25.560] So make sure that you know it's happening
[18:25.560 --> 18:29.000] when the Lambda ran, use environmental variables as well.
[18:29.000 --> 18:31.320] So you can figure out things like what the bucket was
[18:31.320 --> 18:32.880] that it was writing to.
[18:32.880 --> 18:35.520] And then also don't do recursive code.
[18:35.520 --> 18:37.360] That's really a no-no.
[18:37.360 --> 18:40.200] You want to write very simple functions with Lambda.
[18:41.320 --> 18:44.440] Few different ways to write Lambda actually would be
[18:44.440 --> 18:46.280] that you can do the console editor,
[18:46.280 --> 18:47.440] which I use all the time.
[18:47.440 --> 18:49.320] I like to actually just play around with it.
[18:49.320 --> 18:51.640] Now the downside is that if you don't,
[18:51.640 --> 18:53.800] if you do need to use custom libraries,
[18:53.800 --> 18:56.600] you're not gonna be able to do it other than using,
[18:56.600 --> 18:58.440] let's say the AWS SDK.
[18:58.440 --> 19:01.600] But for just simple things, it's a great use case.
[19:01.600 --> 19:06.080] Another one is you can just upload it to AWS console.
[19:06.080 --> 19:09.040] And so you can create a deployment package in an IDE.
[19:09.040 --> 19:12.120] Like for example, Visual Studio for.NET,
[19:12.120 --> 19:13.280] you can actually just right click
[19:13.280 --> 19:16.320] and deploy it directly into Lambda.
[19:16.320 --> 19:20.920] Another one is you can upload the entire package into S3
[19:20.920 --> 19:22.200] and put it into a bucket.
[19:22.200 --> 19:26.280] And then Lambda will just grab it outside of that S3 package.
[19:26.280 --> 19:29.760] A few different things to remember about Lambda.
[19:29.760 --> 19:32.520] The memory and the timeout are configurations
[19:32.520 --> 19:35.840] that determine how the Lambda function performs.
[19:35.840 --> 19:38.440] And these will affect the billing.
[19:38.440 --> 19:40.200] Now, one of the great things about Lambda
[19:40.200 --> 19:43.640] is just amazingly inexpensive to run.
[19:43.640 --> 19:45.560] And the reason is that you're charged
[19:45.560 --> 19:48.200] based on the number of requests for a function.
[19:48.200 --> 19:50.560] A few different things to remember would be the memory.
[19:50.560 --> 19:53.560] Like so if you specify more memory,
[19:53.560 --> 19:57.120] it's going to increase the cost timeout.
[19:57.120 --> 19:59.960] You can also control the memory duration of the function
[19:59.960 --> 20:01.720] by having the right kind of timeout.
[20:01.720 --> 20:03.960] But if you make the timeout too long,
[20:03.960 --> 20:05.880] it could cost you more money.
[20:05.880 --> 20:08.520] So really the best practices would be test the performance
[20:08.520 --> 20:12.880] of Lambda and make sure you have the optimum memory size.
[20:12.880 --> 20:15.160] Also load test it to make sure
[20:15.160 --> 20:17.440] that you understand how the timeouts work.
[20:17.440 --> 20:18.280] Just in general,
[20:18.280 --> 20:21.640] anything with cloud computing, you should load test it.
[20:21.640 --> 20:24.200] Now let's talk about an important topic
[20:24.200 --> 20:25.280] that's a final topic here,
[20:25.280 --> 20:29.080] which is how to deploy Lambda functions.
[20:29.080 --> 20:32.200] So versions are immutable copies of a code
[20:32.200 --> 20:34.200] in the configuration of your Lambda function.
[20:34.200 --> 20:35.880] And the versioning will allow you to publish
[20:35.880 --> 20:39.360] one or more versions of your Lambda function.
[20:39.360 --> 20:40.400] And as a result,
[20:40.400 --> 20:43.360] you can work with different variations of your Lambda function
[20:44.560 --> 20:45.840] in your development workflow,
[20:45.840 --> 20:48.680] like development, beta, production, et cetera.
[20:48.680 --> 20:50.320] And when you create a Lambda function,
[20:50.320 --> 20:52.960] there's only one version, the latest version,
[20:52.960 --> 20:54.080] dollar sign, latest.
[20:54.080 --> 20:57.240] And you can refer to this function using the ARN
[20:57.240 --> 20:59.240] or Amazon resource name.
[20:59.240 --> 21:00.640] And when you publish a new version,
[21:00.640 --> 21:02.920] AWS Lambda will make a snapshot
[21:02.920 --> 21:05.320] of the latest version to create a new version.
[21:06.800 --> 21:09.600] You can also create an alias for Lambda function.
[21:09.600 --> 21:12.280] And conceptually, an alias is just like a pointer
[21:12.280 --> 21:13.800] to a specific function.
[21:13.800 --> 21:17.040] And you can use that alias in the ARN
[21:17.040 --> 21:18.680] to reference the Lambda function version
[21:18.680 --> 21:21.280] that's currently associated with the alias.
[21:21.280 --> 21:23.400] What's nice about the alias is you can roll back
[21:23.400 --> 21:25.840] and forth between different versions,
[21:25.840 --> 21:29.760] which is pretty nice because in the case of deploying
[21:29.760 --> 21:32.920] a new version, if there's a huge problem with it,
[21:32.920 --> 21:34.080] you just toggle it right back.
[21:34.080 --> 21:36.400] And there's really not a big issue
[21:36.400 --> 21:39.400] in terms of rolling back your code.
[21:39.400 --> 21:44.400] Now, let's take a look at an example where AWS S3,
[21:45.160 --> 21:46.720] or Amazon S3 is the event source
[21:46.720 --> 21:48.560] that invokes your Lambda function.
[21:48.560 --> 21:50.720] Every time a new object is created,
[21:50.720 --> 21:52.880] when Amazon S3 is the event source,
[21:52.880 --> 21:55.800] you can store the information for the event source mapping
[21:55.800 --> 21:59.040] in the configuration for the bucket notifications.
[21:59.040 --> 22:01.000] And then in that configuration,
[22:01.000 --> 22:04.800] you could identify the Lambda function ARN
[22:04.800 --> 22:07.160] that Amazon S3 can invoke.
[22:07.160 --> 22:08.520] But in some cases,
[22:08.520 --> 22:11.680] you're gonna have to update the notification configuration.
[22:11.680 --> 22:14.720] So Amazon S3 will invoke the correct version each time
[22:14.720 --> 22:17.840] you publish a new version of your Lambda function.
[22:17.840 --> 22:21.800] So basically, instead of specifying the function ARN,
[22:21.800 --> 22:23.880] you can specify an alias ARN
[22:23.880 --> 22:26.320] in the notification of configuration.
[22:26.320 --> 22:29.160] And as you promote a new version of the Lambda function
[22:29.160 --> 22:32.200] into production, you only need to update the prod alias
[22:32.200 --> 22:34.520] to point to the latest stable version.
[22:34.520 --> 22:36.320] And you also don't need to update
[22:36.320 --> 22:39.120] the notification configuration in Amazon S3.
[22:40.480 --> 22:43.080] And when you build serverless applications
[22:43.080 --> 22:46.600] as common to have code that's shared across Lambda functions,
[22:46.600 --> 22:49.400] it could be custom code, it could be a standard library,
[22:49.400 --> 22:50.560] et cetera.
[22:50.560 --> 22:53.320] And before, and this was really a big limitation,
[22:53.320 --> 22:55.920] was you had to have all the code deployed together.
[22:55.920 --> 22:58.960] But now, one of the really cool things you can do
[22:58.960 --> 23:00.880] is you can have a Lambda function
[23:00.880 --> 23:03.600] to include additional code as a layer.
[23:03.600 --> 23:05.520] So layer is basically a zip archive
[23:05.520 --> 23:08.640] that contains a library, maybe a custom runtime.
[23:08.640 --> 23:11.720] Maybe it isn't gonna include some kind of really cool
[23:11.720 --> 23:13.040] pre-trained model.
[23:13.040 --> 23:14.680] And then the layers you can use,
[23:14.680 --> 23:15.800] the libraries in your function
[23:15.800 --> 23:18.960] without needing to include them in your deployment package.
[23:18.960 --> 23:22.400] And it's a best practice to have the smaller deployment packages
[23:22.400 --> 23:25.240] and share common dependencies with the layers.
[23:26.120 --> 23:28.520] Also layers will help you keep your deployment package
[23:28.520 --> 23:29.360] really small.
[23:29.360 --> 23:32.680] So for node, JS, Python, Ruby functions,
[23:32.680 --> 23:36.000] you can develop your function code in the console
[23:36.000 --> 23:39.000] as long as you keep the package under three megabytes.
[23:39.000 --> 23:42.320] And then a function can use up to five layers at a time,
[23:42.320 --> 23:44.160] which is pretty incredible actually,
[23:44.160 --> 23:46.040] which means that you could have, you know,
[23:46.040 --> 23:49.240] basically up to a 250 megabytes total.
[23:49.240 --> 23:53.920] So for many languages, this is plenty of space.
[23:53.920 --> 23:56.620] Also Amazon has published a public layer
[23:56.620 --> 23:58.800] that includes really popular libraries
[23:58.800 --> 24:00.800] like NumPy and SciPy,
[24:00.800 --> 24:04.840] which does dramatically help data processing
[24:04.840 --> 24:05.680] in machine learning.
[24:05.680 --> 24:07.680] Now, if I had to predict the future
[24:07.680 --> 24:11.840] and I wanted to predict a massive announcement,
[24:11.840 --> 24:14.840] I would say that what AWS could do
[24:14.840 --> 24:18.600] is they could have a GPU enabled layer at some point
[24:18.600 --> 24:20.160] that would include pre-trained models.
[24:20.160 --> 24:22.120] And if they did something like that,
[24:22.120 --> 24:24.320] that could really open up the doors
[24:24.320 --> 24:27.000] for the pre-trained model revolution.
[24:27.000 --> 24:30.160] And I would bet that that's possible.
[24:30.160 --> 24:32.200] All right, well, in a nutshell,
[24:32.200 --> 24:34.680] AWS Lambda is one of my favorite services.
[24:34.680 --> 24:38.440] And I think it's worth everybody's time
[24:38.440 --> 24:42.360] that's interested in AWS to play around with AWS Lambda.
[24:42.360 --> 24:47.200] All right, next week, I'm going to cover API Gateway.
[24:47.200 --> 25:13.840] All right, see you next week.
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
Essentials of MLOps with Azure and Databricks: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-1-introduction/essentials-of-mlops-with-azure
O'Reilly Book: Implementing MLOps in the Enterprise
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
[00:00.000 --> 00:02.260] Hey, three, two, one, there we go, we're live.
[00:02.260 --> 00:07.260] All right, so welcome Simon to Enterprise ML Ops interviews.
[00:09.760 --> 00:13.480] The goal of these interviews is to get people exposed
[00:13.480 --> 00:17.680] to real professionals who are doing work in ML Ops.
[00:17.680 --> 00:20.360] It's such a cutting edge field
[00:20.360 --> 00:22.760] that I think a lot of people are very curious about.
[00:22.760 --> 00:23.600] What is it?
[00:23.600 --> 00:24.960] You know, how do you do it?
[00:24.960 --> 00:27.760] And very honored to have Simon here.
[00:27.760 --> 00:29.200] And do you wanna introduce yourself
[00:29.200 --> 00:31.520] and maybe talk a little bit about your background?
[00:31.520 --> 00:32.360] Sure.
[00:32.360 --> 00:33.960] Yeah, thanks again for inviting me.
[00:34.960 --> 00:38.160] My name is Simon Stebelena or Simon.
[00:38.160 --> 00:40.440] I am originally from Austria,
[00:40.440 --> 00:43.120] but currently working in the Netherlands and Amsterdam
[00:43.120 --> 00:46.080] at Transaction Monitoring Netherlands.
[00:46.080 --> 00:48.780] Here I am the lead ML Ops engineer.
[00:49.840 --> 00:51.680] What are we doing at TML actually?
[00:51.680 --> 00:55.560] We are a data processing company actually.
[00:55.560 --> 00:59.320] We are owned by the five large banks of Netherlands.
[00:59.320 --> 01:02.080] And our purpose is kind of what the name says.
[01:02.080 --> 01:05.920] We are basically lifting specifically anti money laundering.
[01:05.920 --> 01:08.040] So anti money laundering models that run
[01:08.040 --> 01:11.440] on a personalized transactions of businesses
[01:11.440 --> 01:13.240] we get from these five banks
[01:13.240 --> 01:15.760] to detect unusual patterns on that transaction graph
[01:15.760 --> 01:19.000] that might indicate money laundering.
[01:19.000 --> 01:20.520] That's a natural what we do.
[01:20.520 --> 01:21.800] So as you can imagine,
[01:21.800 --> 01:24.160] we are really focused on building models
[01:24.160 --> 01:27.280] and obviously ML Ops is a big component there
[01:27.280 --> 01:29.920] because that is really the core of what you do.
[01:29.920 --> 01:32.680] You wanna do it efficiently and effectively as well.
[01:32.680 --> 01:34.760] In my role as lead ML Ops engineer,
[01:34.760 --> 01:36.880] I'm on the one hand the lead engineer
[01:36.880 --> 01:38.680] of the actual ML Ops platform team.
[01:38.680 --> 01:40.200] So this is actually a centralized team
[01:40.200 --> 01:42.680] that builds out lots of the infrastructure
[01:42.680 --> 01:47.320] that's needed to do modeling effectively and efficiently.
[01:47.320 --> 01:50.360] But also I am the craft lead
[01:50.360 --> 01:52.640] for the machine learning engineering craft.
[01:52.640 --> 01:55.120] These are actually in our case, the machine learning engineers,
[01:55.120 --> 01:58.360] the people working within the model development teams
[01:58.360 --> 01:59.360] and cross functional teams
[01:59.360 --> 02:01.680] actually building these models.
[02:01.680 --> 02:03.640] That's what I'm currently doing
[02:03.640 --> 02:05.760] during the evenings and weekends.
[02:05.760 --> 02:09.400] I'm also lecturer at the University of Applied Sciences, Vienna.
[02:09.400 --> 02:12.080] And there I'm teaching data mining
[02:12.080 --> 02:15.160] and data warehousing to master students, essentially.
[02:16.240 --> 02:19.080] Before TMNL, I was at bold.com,
[02:19.080 --> 02:21.960] which is the largest eCommerce retailer in the Netherlands.
[02:21.960 --> 02:25.040] So I always tend to see the Amazon of the Netherlands
[02:25.040 --> 02:27.560] or been a lux actually.
[02:27.560 --> 02:30.920] It is still the biggest eCommerce retailer in the Netherlands
[02:30.920 --> 02:32.960] even before Amazon actually.
[02:32.960 --> 02:36.160] And there I was an expert machine learning engineer.
[02:36.160 --> 02:39.240] So doing somewhat comparable stuff,
[02:39.240 --> 02:42.440] a bit more still focused on the actual modeling part.
[02:42.440 --> 02:44.800] Now it's really more on the infrastructure end.
[02:45.760 --> 02:46.760] And well, before that,
[02:46.760 --> 02:49.360] I spent some time in consulting, leading a data science team.
[02:49.360 --> 02:50.880] That's actually where I kind of come from.
[02:50.880 --> 02:53.360] I really come from originally the data science end.
[02:54.640 --> 02:57.840] And there I kind of started drifting towards ML Ops
[02:57.840 --> 02:59.200] because we started building out
[02:59.200 --> 03:01.640] a deployment and serving platform
[03:01.640 --> 03:04.440] that would as consulting company would make it easier
[03:04.440 --> 03:07.920] for us to deploy models for our clients
[03:07.920 --> 03:10.840] to serve these models, to also monitor these models.
[03:10.840 --> 03:12.800] And that kind of then made me drift further and further
[03:12.800 --> 03:15.520] down the engineering lane all the way to ML Ops.
[03:17.000 --> 03:19.600] Great, yeah, that's a great background.
[03:19.600 --> 03:23.200] I'm kind of curious in terms of the data science
[03:23.200 --> 03:25.240] to ML Ops journey,
[03:25.240 --> 03:27.720] that I think would be a great discussion
[03:27.720 --> 03:29.080] to dig into a little bit.
[03:30.280 --> 03:34.320] My background is originally more on the software engineering
[03:34.320 --> 03:36.920] side and when I was in the Bay Area,
[03:36.920 --> 03:41.160] I did individual contributor and then ran companies
[03:41.160 --> 03:44.240] at one point and ran multiple teams.
[03:44.240 --> 03:49.240] And then as the data science field exploded,
[03:49.240 --> 03:52.880] I hired multiple data science teams and worked with them.
[03:52.880 --> 03:55.800] But what was interesting is that I found that
[03:56.840 --> 03:59.520] I think the original approach of data science
[03:59.520 --> 04:02.520] from my perspective was lacking
[04:02.520 --> 04:07.240] in that there wasn't really like deliverables.
[04:07.240 --> 04:10.520] And I think when you look at a software engineering team,
[04:10.520 --> 04:12.240] it's very clear there's deliverables.
[04:12.240 --> 04:14.800] Like you have a mobile app and it has to get better
[04:14.800 --> 04:15.880] each week, right?
[04:15.880 --> 04:18.200] Where else, what are you doing?
[04:18.200 --> 04:20.880] And so I would love to hear your story
[04:20.880 --> 04:25.120] about how you went from doing kind of more pure data science
[04:25.120 --> 04:27.960] to now it sounds like ML Ops.
[04:27.960 --> 04:30.240] Yeah, yeah, actually.
[04:30.240 --> 04:33.800] So back then in consulting one of the,
[04:33.800 --> 04:36.200] which was still at least back then in Austria,
[04:36.200 --> 04:39.280] data science and everything around it was still kind of
[04:39.280 --> 04:43.720] in this infancy back then 2016 and so on.
[04:43.720 --> 04:46.560] It was still really, really new to many organizations,
[04:46.560 --> 04:47.400] at least in Austria.
[04:47.400 --> 04:50.120] There might be some years behind in the US and stuff.
[04:50.120 --> 04:52.040] But back then it was still relatively fresh.
[04:52.040 --> 04:55.240] So in consulting, what we very often struggled with was
[04:55.240 --> 04:58.520] on the modeling end, problems could be solved,
[04:58.520 --> 05:02.040] but actually then easy deployment,
[05:02.040 --> 05:05.600] keeping these models in production at client side.
[05:05.600 --> 05:08.880] That was always a bit more of the challenge.
[05:08.880 --> 05:12.400] And so naturally kind of I started thinking
[05:12.400 --> 05:16.200] and focusing more on the actual bigger problem that I saw,
[05:16.200 --> 05:19.440] which was not so much building the models,
[05:19.440 --> 05:23.080] but it was really more, how can we streamline things?
[05:23.080 --> 05:24.800] How can we keep things operating?
[05:24.800 --> 05:27.960] How can we make that move easier from a prototype,
[05:27.960 --> 05:30.680] from a PUC to a productionized model?
[05:30.680 --> 05:33.160] Also how can we keep it there and maintain it there?
[05:33.160 --> 05:35.480] So personally I was really more,
[05:35.480 --> 05:37.680] I saw that this problem was coming up
[05:38.960 --> 05:40.320] and that really fascinated me.
[05:40.320 --> 05:44.120] So I started jumping more on that exciting problem.
[05:44.120 --> 05:45.080] That's how it went for me.
[05:45.080 --> 05:47.000] And back then we then also recognized it
[05:47.000 --> 05:51.560] as a potential product in our case.
[05:51.560 --> 05:54.120] So we started building out that deployment
[05:54.120 --> 05:56.960] and serving and monitoring platform, actually.
[05:56.960 --> 05:59.520] And that then really for me, naturally,
[05:59.520 --> 06:01.840] I fell into that rabbit hole
[06:01.840 --> 06:04.280] and I also never wanted to get out of it again.
[06:05.680 --> 06:09.400] So the system that you built initially,
[06:09.400 --> 06:10.840] what was your stack?
[06:10.840 --> 06:13.760] What were some of the things you were using?
[06:13.760 --> 06:17.000] Yeah, so essentially we had,
[06:17.000 --> 06:19.560] when we talk about the stack on the backend,
[06:19.560 --> 06:20.560] there was a lot of,
[06:20.560 --> 06:23.000] so the full backend was written in Java.
[06:23.000 --> 06:25.560] We were using more from a user perspective,
[06:25.560 --> 06:28.040] the contract that we kind of had,
[06:28.040 --> 06:32.560] our goal was to build a drag and drop platform for models.
[06:32.560 --> 06:35.760] So basically the contract was you package your model
[06:35.760 --> 06:37.960] as an MLflow model,
[06:37.960 --> 06:41.520] and then you basically drag and drop it into a web UI.
[06:41.520 --> 06:43.640] It's gonna be wrapped in containers.
[06:43.640 --> 06:45.040] It's gonna be deployed.
[06:45.040 --> 06:45.880] It's gonna be,
[06:45.880 --> 06:49.680] there will be a monitoring layer in front of it
[06:49.680 --> 06:52.760] based on whatever the dataset is you trained it on.
[06:52.760 --> 06:55.920] You would automatically calculate different metrics,
[06:55.920 --> 06:57.360] different distributional metrics
[06:57.360 --> 06:59.240] around your variables that you are using.
[06:59.240 --> 07:02.080] And so we were layering this approach
[07:02.080 --> 07:06.840] to, so that eventually every incoming request would be,
[07:06.840 --> 07:08.160] you would have a nice dashboard.
[07:08.160 --> 07:10.040] You could monitor all that stuff.
[07:10.040 --> 07:12.600] So stackwise it was actually MLflow.
[07:12.600 --> 07:15.480] Specifically MLflow models a lot.
[07:15.480 --> 07:17.920] Then it was Java in the backend, Python.
[07:17.920 --> 07:19.760] There was a lot of Python,
[07:19.760 --> 07:22.040] especially PySpark component as well.
[07:23.000 --> 07:25.880] There was a, it's been quite a while actually,
[07:25.880 --> 07:29.160] there was a quite some part written in Scala.
[07:29.160 --> 07:32.280] Also, because there was a component of this platform
[07:32.280 --> 07:34.800] was also a bit of an auto ML approach,
[07:34.800 --> 07:36.480] but that died then over time.
[07:36.480 --> 07:40.120] And that was also based on PySpark
[07:40.120 --> 07:43.280] and vanilla Spark written in Scala.
[07:43.280 --> 07:45.560] So we could facilitate the auto ML part.
[07:45.560 --> 07:48.600] And then later on we actually added that deployment,
[07:48.600 --> 07:51.480] the easy deployment and serving part.
[07:51.480 --> 07:55.280] So that was kind of, yeah, a lot of custom build stuff.
[07:55.280 --> 07:56.120] Back then, right?
[07:56.120 --> 07:59.720] There wasn't that much MLOps tooling out there yet.
[07:59.720 --> 08:02.920] So you need to build a lot of that stuff custom.
[08:02.920 --> 08:05.280] So it was largely custom built.
[08:05.280 --> 08:09.280] Yeah, the MLflow concept is an interesting concept
[08:09.280 --> 08:13.880] because they provide this package structure
[08:13.880 --> 08:17.520] that at least you have some idea of,
[08:17.520 --> 08:19.920] what is gonna be sent into the model
[08:19.920 --> 08:22.680] and like there's a format for the model.
[08:22.680 --> 08:24.720] And I think that part of MLflow
[08:24.720 --> 08:27.520] seems to be a pretty good idea,
[08:27.520 --> 08:30.080] which is you're creating a standard where,
[08:30.080 --> 08:32.360] you know, if in the case of,
[08:32.360 --> 08:34.720] if you're using scikit learn or something,
[08:34.720 --> 08:37.960] you don't necessarily want to just throw
[08:37.960 --> 08:40.560] like a pickled model somewhere and just say,
[08:40.560 --> 08:42.720] okay, you know, let's go.
[08:42.720 --> 08:44.760] Yeah, that was also our thinking back then.
[08:44.760 --> 08:48.040] So we thought a lot about what would be a,
[08:48.040 --> 08:51.720] what would be, what could become the standard actually
[08:51.720 --> 08:53.920] for how you package models.
[08:53.920 --> 08:56.200] And back then MLflow was one of the little tools
[08:56.200 --> 08:58.160] that was already there, already existent.
[08:58.160 --> 09:00.360] And of course there was data bricks behind it.
[09:00.360 --> 09:02.680] So we also made a bet on that back then and said,
[09:02.680 --> 09:04.920] all right, let's follow that packaging standard
[09:04.920 --> 09:08.680] and make it the contract how you would as a data scientist,
[09:08.680 --> 09:10.800] then how you would need to package it up
[09:10.800 --> 09:13.640] and submit it to the platform.
[09:13.640 --> 09:16.800] Yeah, it's interesting because the,
[09:16.800 --> 09:19.560] one of the, this reminds me of one of the issues
[09:19.560 --> 09:21.800] that's happening right now with cloud computing,
[09:21.800 --> 09:26.800] where in the cloud AWS has dominated for a long time
[09:29.480 --> 09:34.480] and they have 40% market share, I think globally.
[09:34.480 --> 09:38.960] And Azure's now gaining and they have some pretty good traction
[09:38.960 --> 09:43.120] and then GCP's been down for a bit, you know,
[09:43.120 --> 09:45.760] in that maybe the 10% range or something like that.
[09:45.760 --> 09:47.760] But what's interesting is that it seems like
[09:47.760 --> 09:51.480] in the case of all of the cloud providers,
[09:51.480 --> 09:54.360] they haven't necessarily been leading the way
[09:54.360 --> 09:57.840] on things like packaging models, right?
[09:57.840 --> 10:01.480] Or, you know, they have their own proprietary systems
[10:01.480 --> 10:06.480] which have been developed and are continuing to be developed
[10:06.640 --> 10:08.920] like Vertex AI in the case of Google,
[10:09.760 --> 10:13.160] the SageMaker in the case of Amazon.
[10:13.160 --> 10:16.480] But what's interesting is, let's just take SageMaker,
[10:16.480 --> 10:20.920] for example, there isn't really like this, you know,
[10:20.920 --> 10:25.480] industry wide standard of model packaging
[10:25.480 --> 10:28.680] that SageMaker uses, they have their own proprietary stuff
[10:28.680 --> 10:31.040] that kind of builds in and Vertex AI
[10:31.040 --> 10:32.440] has their own proprietary stuff.
[10:32.440 --> 10:34.920] So, you know, I think it is interesting
[10:34.920 --> 10:36.960] to see what's gonna happen
[10:36.960 --> 10:41.120] because I think your original hypothesis which is,
[10:41.120 --> 10:44.960] let's pick, you know, this looks like it's got some traction
[10:44.960 --> 10:48.760] and it wasn't necessarily tied directly to a cloud provider
[10:48.760 --> 10:51.600] because Databricks can work on anything.
[10:51.600 --> 10:53.680] It seems like that in particular,
[10:53.680 --> 10:56.800] that's one of the more sticky problems right now
[10:56.800 --> 11:01.800] with MLopsis is, you know, who's the leader?
[11:02.280 --> 11:05.440] Like, who's developing the right, you know,
[11:05.440 --> 11:08.880] kind of a standard for tooling.
[11:08.880 --> 11:12.320] And I don't know, maybe that leads into kind of you talking
[11:12.320 --> 11:13.760] a little bit about what you're doing currently.
[11:13.760 --> 11:15.600] Like, do you have any thoughts about the, you know,
[11:15.600 --> 11:18.720] current tooling and what you're doing at your current company
[11:18.720 --> 11:20.920] and what's going on with that?
[11:20.920 --> 11:21.760] Absolutely.
[11:21.760 --> 11:24.200] So at my current organization,
[11:24.200 --> 11:26.040] Transaction Monitor Netherlands,
[11:26.040 --> 11:27.480] we are fully on AWS.
[11:27.480 --> 11:32.000] So we're really almost cloud native AWS.
[11:32.000 --> 11:34.840] And so that also means everything we do on the modeling side
[11:34.840 --> 11:36.600] really evolves around SageMaker.
[11:37.680 --> 11:40.840] So for us, specifically for us as MLops team,
[11:40.840 --> 11:44.680] we are building the platform around SageMaker capabilities.
[11:45.680 --> 11:48.360] And on that end, at least company internal,
[11:48.360 --> 11:52.880] we have a contract how you must actually deploy models.
[11:52.880 --> 11:56.200] There is only one way, what we call the golden path,
[11:56.200 --> 11:59.800] in that case, this is the streamlined highly automated path
[11:59.800 --> 12:01.360] that is supported by the platform.
[12:01.360 --> 12:04.360] This is the only way how you can actually deploy models.
[12:04.360 --> 12:09.360] And in our case, that is actually a SageMaker pipeline object.
[12:09.640 --> 12:12.680] So in our company, we're doing large scale batch processing.
[12:12.680 --> 12:15.040] So we're actually not doing anything real time at present.
[12:15.040 --> 12:17.040] We are doing post transaction monitoring.
[12:17.040 --> 12:20.960] So that means you need to submit essentially DAX, right?
[12:20.960 --> 12:23.400] This is what we use for training.
[12:23.400 --> 12:25.680] This is what we also deploy eventually.
[12:25.680 --> 12:27.720] And this is our internal contract.
[12:27.720 --> 12:32.200] You need to provision a SageMaker in your model repository.
[12:32.200 --> 12:34.640] You got to have one place,
[12:34.640 --> 12:37.840] and there must be a function with a specific name
[12:37.840 --> 12:41.440] and that function must return a SageMaker pipeline object.
[12:41.440 --> 12:44.920] So this is our internal contract actually.
[12:44.920 --> 12:46.600] Yeah, that's interesting.
[12:46.600 --> 12:51.200] I mean, and I could see like for, I know many people
[12:51.200 --> 12:53.880] that are using SageMaker in production,
[12:53.880 --> 12:58.680] and it does seem like where it has some advantages
[12:58.680 --> 13:02.360] is that AWS generally does a pretty good job
[13:02.360 --> 13:04.240] at building solutions.
[13:04.240 --> 13:06.920] And if you just look at the history of services,
[13:06.920 --> 13:09.080] the odds are pretty high
[13:09.080 --> 13:12.880] that they'll keep getting better, keep improving things.
[13:12.880 --> 13:17.080] And it seems like what I'm hearing from people,
[13:17.080 --> 13:19.080] and it sounds like maybe with your organization as well,
[13:19.080 --> 13:24.080] is that potentially the SDK for SageMaker
[13:24.440 --> 13:29.120] is really the win versus some of the UX tools they have
[13:29.120 --> 13:32.680] and the interface for Canvas and Studio.
[13:32.680 --> 13:36.080] Is that what's happening?
[13:36.080 --> 13:38.720] Yeah, so I think, right,
[13:38.720 --> 13:41.440] what we try to do is we always try to think about our users.
[13:41.440 --> 13:44.880] So how do our users, who are our users?
[13:44.880 --> 13:47.000] What capabilities and skills do they have?
[13:47.000 --> 13:50.080] And what freedom should they have
[13:50.080 --> 13:52.640] and what abilities should they have to develop models?
[13:52.640 --> 13:55.440] In our case, we don't really have use cases
[13:55.440 --> 13:58.640] for stuff like Canvas because our users
[13:58.640 --> 14:02.680] are fairly mature teams that know how to do their,
[14:02.680 --> 14:04.320] on the one hand, the data science stuff, of course,
[14:04.320 --> 14:06.400] but also the engineering stuff.
[14:06.400 --> 14:08.160] So in our case, things like Canvas
[14:08.160 --> 14:10.320] do not really play so much role
[14:10.320 --> 14:12.960] because obviously due to the high abstraction layer
[14:12.960 --> 14:15.640] of more like graphical user interfaces,
[14:15.640 --> 14:17.360] drag and drop tooling,
[14:17.360 --> 14:20.360] you are also limited in what you can do,
[14:20.360 --> 14:22.480] or what you can do easily.
[14:22.480 --> 14:26.320] So in our case, really, it is the strength of the flexibility
[14:26.320 --> 14:28.320] that the SageMaker SDK gives you.
[14:28.320 --> 14:33.040] And in general, the SDK around most AWS services.
[14:34.080 --> 14:36.760] But also it comes with challenges, of course.
[14:37.720 --> 14:38.960] You give a lot of freedom,
[14:38.960 --> 14:43.400] but also you're creating a certain ask,
[14:43.400 --> 14:47.320] certain requirements for your model development teams,
[14:47.320 --> 14:49.600] which is also why we've also been working
[14:49.600 --> 14:52.600] about abstracting further away from the SDK.
[14:52.600 --> 14:54.600] So our objective is actually
[14:54.600 --> 14:58.760] that you should not be forced to interact with the raw SDK
[14:58.760 --> 15:00.600] when you use SageMaker anymore,
[15:00.600 --> 15:03.520] but you have a thin layer of abstraction
[15:03.520 --> 15:05.480] on top of what you are doing.
[15:05.480 --> 15:07.480] That's actually something we are moving towards
[15:07.480 --> 15:09.320] more and more as well.
[15:09.320 --> 15:11.120] Because yeah, it gives you the flexibility,
[15:11.120 --> 15:12.960] but also flexibility comes at a cost,
[15:12.960 --> 15:15.080] comes often at the cost of speeds,
[15:15.080 --> 15:18.560] specifically when it comes to the 90% default stuff
[15:18.560 --> 15:20.720] that you want to do, yeah.
[15:20.720 --> 15:24.160] And one of the things that I have as a complaint
[15:24.160 --> 15:29.160] against SageMaker is that it only uses virtual machines,
[15:30.000 --> 15:35.000] and it does seem like a strange strategy in some sense.
[15:35.000 --> 15:40.000] Like for example, I guess if you're doing batch only,
[15:40.000 --> 15:42.000] it doesn't matter as much,
[15:42.000 --> 15:45.000] which I think is a good strategy actually
[15:45.000 --> 15:50.000] to get your batch based predictions very, very strong.
[15:50.000 --> 15:53.000] And in that case, maybe the virtual machines
[15:53.000 --> 15:56.000] make a little bit less of a complaint.
[15:56.000 --> 16:00.000] But in the case of the endpoints with SageMaker,
[16:00.000 --> 16:02.000] the fact that you have to spend up
[16:02.000 --> 16:04.000] these really expensive virtual machines
[16:04.000 --> 16:08.000] and let them run 24 seven to do online prediction,
[16:08.000 --> 16:11.000] is that something that your organization evaluated
[16:11.000 --> 16:13.000] and decided not to use?
[16:13.000 --> 16:15.000] Or like, what are your thoughts behind that?
[16:15.000 --> 16:19.000] Yeah, in our case, doing real time
[16:19.000 --> 16:22.000] or near real time inference is currently not really relevant
[16:22.000 --> 16:25.000] for the simple reason that when you think a bit more
[16:25.000 --> 16:28.000] about the money laundering or anti money laundering space,
[16:28.000 --> 16:31.000] typically when, right,
[16:31.000 --> 16:34.000] all every individual bank must do anti money laundering
[16:34.000 --> 16:37.000] and they have armies of people doing that.
[16:37.000 --> 16:39.000] But on the other hand,
[16:39.000 --> 16:43.000] the time it actually takes from one of their systems,
[16:43.000 --> 16:46.000] one of their AML systems actually detecting something
[16:46.000 --> 16:49.000] that's unusual that then goes into a review process
[16:49.000 --> 16:54.000] until it eventually hits the governmental institution
[16:54.000 --> 16:56.000] that then takes care of the cases that have been
[16:56.000 --> 16:58.000] at least twice validated that they are indeed,
[16:58.000 --> 17:01.000] they look very unusual.
[17:01.000 --> 17:04.000] So this takes a while, this can take quite some time,
[17:04.000 --> 17:06.000] which is also why it doesn't really matter
[17:06.000 --> 17:09.000] whether you ship your prediction within a second
[17:09.000 --> 17:13.000] or whether it takes you a week or two weeks.
[17:13.000 --> 17:15.000] It doesn't really matter, hence for us,
[17:15.000 --> 17:19.000] that problem so far thinking about real time inference
[17:19.000 --> 17:21.000] has not been there.
[17:21.000 --> 17:25.000] But yeah, indeed, for other use cases,
[17:25.000 --> 17:27.000] for also private projects,
[17:27.000 --> 17:29.000] we've also been considering SageMaker Endpoints
[17:29.000 --> 17:31.000] for a while, but exactly what you said,
[17:31.000 --> 17:33.000] the fact that you need to have a very beefy machine
[17:33.000 --> 17:35.000] running all the time,
[17:35.000 --> 17:39.000] specifically when you have heavy GPU loads, right,
[17:39.000 --> 17:43.000] and you're actually paying for that machine running 2047,
[17:43.000 --> 17:46.000] although you do have quite fluctuating load.
[17:46.000 --> 17:49.000] Yeah, then that definitely becomes quite a consideration
[17:49.000 --> 17:51.000] of what you go for.
[17:51.000 --> 17:58.000] Yeah, and I actually have been talking to AWS about that,
[17:58.000 --> 18:02.000] because one of the issues that I have is that
[18:02.000 --> 18:07.000] the AWS platform really pushes serverless,
[18:07.000 --> 18:10.000] and then my question for AWS is,
[18:10.000 --> 18:13.000] so why aren't you using it?
[18:13.000 --> 18:16.000] I mean, if you're pushing serverless for everything,
[18:16.000 --> 18:19.000] why is SageMaker nothing serverless?
[18:19.000 --> 18:21.000] And so maybe they're going to do that, I don't know.
[18:21.000 --> 18:23.000] I don't have any inside information,
[18:23.000 --> 18:29.000] but it is interesting to hear you had some similar concerns.
[18:29.000 --> 18:32.000] I know that there's two questions here.
[18:32.000 --> 18:37.000] One is someone asked about what do you do for data versioning,
[18:37.000 --> 18:41.000] and a second one is how do you do event based MLOps?
[18:41.000 --> 18:43.000] So maybe kind of following up.
[18:43.000 --> 18:46.000] Yeah, what do we do for data versioning?
[18:46.000 --> 18:51.000] On the one hand, we're running a data lakehouse,
[18:51.000 --> 18:54.000] where after data we get from the financial institutions,
[18:54.000 --> 18:57.000] from the banks that runs through massive data pipeline,
[18:57.000 --> 19:01.000] also on AWS, we're using glue and step functions actually for that,
[19:01.000 --> 19:03.000] and then eventually it ends up modeled to some extent,
[19:03.000 --> 19:06.000] sanitized, quality checked in our data lakehouse,
[19:06.000 --> 19:10.000] and there we're actually using hoodie on top of S3.
[19:10.000 --> 19:13.000] And this is also what we use for versioning,
[19:13.000 --> 19:16.000] which we use for time travel and all these things.
[19:16.000 --> 19:19.000] So that is hoodie on top of S3,
[19:19.000 --> 19:21.000] when then pipelines,
[19:21.000 --> 19:24.000] so actually our model pipelines plug in there
[19:24.000 --> 19:27.000] and spit out predictions, alerts,
[19:27.000 --> 19:29.000] what we call alerts eventually.
[19:29.000 --> 19:33.000] That is something that we version based on unique IDs.
[19:33.000 --> 19:36.000] So processing IDs, we track pretty much everything,
[19:36.000 --> 19:39.000] every line of code that touched,
[19:39.000 --> 19:43.000] is related to a specific row in our data.
[19:43.000 --> 19:46.000] So we can exactly track back for every single row
[19:46.000 --> 19:48.000] in our predictions and in our alerts,
[19:48.000 --> 19:50.000] what pipeline ran on it,
[19:50.000 --> 19:52.000] which jobs were in that pipeline,
[19:52.000 --> 19:56.000] which code exactly was running in each job,
[19:56.000 --> 19:58.000] which intermediate results were produced.
[19:58.000 --> 20:01.000] So we're basically adding lineage information
[20:01.000 --> 20:03.000] to everything we output along that line,
[20:03.000 --> 20:05.000] so we can track everything back
[20:05.000 --> 20:09.000] using a few tools we've built.
[20:09.000 --> 20:12.000] So the tool you mentioned,
[20:12.000 --> 20:13.000] I'm not familiar with it.
[20:13.000 --> 20:14.000] What is it called again?
[20:14.000 --> 20:15.000] It's called hoodie?
[20:15.000 --> 20:16.000] Hoodie.
[20:16.000 --> 20:17.000] Hoodie.
[20:17.000 --> 20:18.000] Oh, what is it?
[20:18.000 --> 20:19.000] Maybe you can describe it.
[20:19.000 --> 20:22.000] Yeah, hoodie is essentially,
[20:22.000 --> 20:29.000] it's quite similar to other tools such as
[20:29.000 --> 20:31.000] Databricks, how is it called?
[20:31.000 --> 20:32.000] Databricks?
[20:32.000 --> 20:33.000] Delta Lake maybe?
[20:33.000 --> 20:34.000] Yes, exactly.
[20:34.000 --> 20:35.000] Exactly.
[20:35.000 --> 20:38.000] It's basically, it's equivalent to Delta Lake,
[20:38.000 --> 20:40.000] just back then when we looked into
[20:40.000 --> 20:42.000] what are we going to use.
[20:42.000 --> 20:44.000] Delta Lake was not open sourced yet.
[20:44.000 --> 20:46.000] Databricks open sourced a while ago.
[20:46.000 --> 20:47.000] We went for Hoodie.
[20:47.000 --> 20:50.000] It essentially, it is a layer on top of,
[20:50.000 --> 20:53.000] in our case, S3 that allows you
[20:53.000 --> 20:58.000] to more easily keep track of what you,
[20:58.000 --> 21:03.000] of the actions you are performing on your data.
[21:03.000 --> 21:08.000] So it's essentially very similar to Delta Lake,
[21:08.000 --> 21:13.000] just already before an open sourced solution.
[21:13.000 --> 21:15.000] Yeah, that's, I didn't know anything about that.
[21:15.000 --> 21:16.000] So now I do.
[21:16.000 --> 21:19.000] So thanks for letting me know.
[21:19.000 --> 21:21.000] I'll have to look into that.
[21:21.000 --> 21:27.000] The other, I guess, interesting stack related question is,
[21:27.000 --> 21:29.000] what are your thoughts about,
[21:29.000 --> 21:32.000] I think there's two areas that I think
[21:32.000 --> 21:34.000] are interesting and that are emerging.
[21:34.000 --> 21:36.000] Oh, actually there's, there's multiple.
[21:36.000 --> 21:37.000] Maybe I'll just bring them all up.
[21:37.000 --> 21:39.000] So we'll do one by one.
[21:39.000 --> 21:42.000] So these are some emerging areas that I'm, that I'm seeing.
[21:42.000 --> 21:49.000] So one is the concept of event driven, you know,
[21:49.000 --> 21:54.000] architecture versus, versus maybe like a static architecture.
[21:54.000 --> 21:57.000] And so I think obviously you're using step functions.
[21:57.000 --> 22:00.000] So you're a fan of, of event driven architecture.
[22:00.000 --> 22:04.000] Maybe we start, we'll start with that one is what are your,
[22:04.000 --> 22:08.000] what are your thoughts on going more event driven in your organization?
[22:08.000 --> 22:09.000] Yeah.
[22:09.000 --> 22:13.000] In, in, in our case, essentially everything works event driven.
[22:13.000 --> 22:14.000] Right.
[22:14.000 --> 22:19.000] So since we on AWS, we're using event bridge or cloud watch events.
[22:19.000 --> 22:21.000] I think now it's called everywhere.
[22:21.000 --> 22:22.000] Right.
[22:22.000 --> 22:24.000] This is how we trigger pretty much everything in our stack.
[22:24.000 --> 22:27.000] This is how we trigger our data pipelines when data comes in.
[22:27.000 --> 22:32.000] This is how we trigger different, different lambdas that parse our
[22:32.000 --> 22:35.000] certain information from your log, store them in different databases.
[22:35.000 --> 22:40.000] This is how we also, how we, at some point in the back in the past,
[22:40.000 --> 22:44.000] how we also triggered new deployments when new models were approved in
[22:44.000 --> 22:46.000] your model registry.
[22:46.000 --> 22:50.000] So basically everything we've been doing is, is fully event driven.
[22:50.000 --> 22:51.000] Yeah.
[22:51.000 --> 22:56.000] So, so I think this is a key thing you bring up here is that I've,
[22:56.000 --> 23:00.000] I've talked to many people who don't use AWS, who are, you know,
[23:00.000 --> 23:03.000] all alternatively experts at technology.
[23:03.000 --> 23:06.000] And one of the things that I've heard some people say is like, oh,
[23:06.000 --> 23:13.000] well, AWS is in as fast as X or Y, like Lambda is in as fast as X or Y or,
[23:13.000 --> 23:17.000] you know, Kubernetes or, but, but the point you bring up is exactly the
[23:17.000 --> 23:24.000] way I think about AWS is that the true advantage of AWS platform is the,
[23:24.000 --> 23:29.000] is the tight integration with the services and you can design event
[23:29.000 --> 23:31.000] driven workflows.
[23:31.000 --> 23:33.000] Would you say that's, that's absolutely.
[23:33.000 --> 23:34.000] Yeah.
[23:34.000 --> 23:35.000] Yeah.
[23:35.000 --> 23:39.000] I think designing event driven workflows on AWS is incredibly easy to do.
[23:39.000 --> 23:40.000] Yeah.
[23:40.000 --> 23:43.000] And it also comes incredibly natural and that's extremely powerful.
[23:43.000 --> 23:44.000] Right.
[23:44.000 --> 23:49.000] And simply by, by having an easy way how to trigger lambdas event driven,
[23:49.000 --> 23:52.000] you can pretty much, right, pretty much do everything and glue
[23:52.000 --> 23:54.000] everything together that you want.
[23:54.000 --> 23:56.000] I think that gives you a tremendous flexibility.
[23:56.000 --> 23:57.000] Yeah.
[23:57.000 --> 24:00.000] So, so I think there's two things that come to mind now.
[24:00.000 --> 24:07.000] One is that, that if you are developing an ML ops platform that you
[24:07.000 --> 24:09.000] can't ignore Lambda.
[24:09.000 --> 24:12.000] So I, because I've had some people tell me, oh, well, we can do this and
[24:12.000 --> 24:13.000] this and this better.
[24:13.000 --> 24:17.000] It's like, yeah, but if you're going to be on AWS, you have to understand
[24:17.000 --> 24:18.000] why people use Lambda.
[24:18.000 --> 24:19.000] It isn't speed.
[24:19.000 --> 24:24.000] It's, it's the ease of, ease of developing very rich solutions.
[24:24.000 --> 24:25.000] Right.
[24:25.000 --> 24:26.000] Absolutely.
[24:26.000 --> 24:28.000] And then the glue between, between what you are building eventually.
[24:28.000 --> 24:33.000] And you can even almost your, the thoughts in your mind turn into Lambda.
[24:33.000 --> 24:36.000] You know, like you can be thinking and building code so quickly.
[24:36.000 --> 24:37.000] Absolutely.
[24:37.000 --> 24:41.000] Everything turns into which event do I need to listen to and then I trigger
[24:41.000 --> 24:43.000] a Lambda and that Lambda does this and that.
[24:43.000 --> 24:44.000] Yeah.
[24:44.000 --> 24:48.000] And the other part about Lambda that's pretty, pretty awesome is that it
[24:48.000 --> 24:52.000] hooks into services that have infinite scale.
[24:52.000 --> 24:56.000] Like so SQS, like you can't break SQS.
[24:56.000 --> 24:59.000] Like there's nothing you can do to ever take SQS down.
[24:59.000 --> 25:02.000] It handles unlimited requests in and unlimited requests out.
[25:02.000 --> 25:04.000] How many systems are like that?
[25:04.000 --> 25:05.000] Yeah.
[25:05.000 --> 25:06.000] Yeah, absolutely.
[25:06.000 --> 25:07.000] Yeah.
[25:07.000 --> 25:12.000] So then this kind of a followup would be that, that maybe data scientists
[25:12.000 --> 25:17.000] should learn Lambda and step functions in order to, to get to
[25:17.000 --> 25:18.000] MLOps.
[25:18.000 --> 25:21.000] I think that's a yes.
[25:21.000 --> 25:25.000] If you want to, if you want to put the foot into MLOps and you are on AWS,
[25:25.000 --> 25:31.000] then I think there is no way around learning these fundamentals.
[25:31.000 --> 25:32.000] Right.
[25:32.000 --> 25:35.000] There's no way around learning things like what is a Lambda?
[25:35.000 --> 25:39.000] How do I, how do I create a Lambda via Terraform or whatever tool you're
[25:39.000 --> 25:40.000] using there?
[25:40.000 --> 25:42.000] And how do I hook it up to an event?
[25:42.000 --> 25:47.000] And how do I, how do I use the AWS SDK to interact with different
[25:47.000 --> 25:48.000] services?
[25:48.000 --> 25:49.000] So, right.
[25:49.000 --> 25:53.000] I think if you want to take a step into MLOps from, from coming more from
[25:53.000 --> 25:57.000] the data science and it's extremely important to familiarize yourself
[25:57.000 --> 26:01.000] with how do you, at least the fundamentals, how do you architect
[26:01.000 --> 26:03.000] basic solutions on AWS?
[26:03.000 --> 26:05.000] How do you glue services together?
[26:05.000 --> 26:07.000] How do you make them speak to each other?
[26:07.000 --> 26:09.000] So yeah, I think that's quite fundamental.
[26:09.000 --> 26:14.000] Ideally, ideally, I think that's what the platform should take away from you
[26:14.000 --> 26:16.000] as a, as a pure data scientist.
[26:16.000 --> 26:19.000] You don't, should not necessarily have to deal with that stuff.
[26:19.000 --> 26:23.000] But if you're interested in, if you want to make that move more towards MLOps,
[26:23.000 --> 26:27.000] I think learning about infrastructure and specifically in the context of AWS
[26:27.000 --> 26:31.000] about the services and how to use them is really fundamental.
[26:31.000 --> 26:32.000] Yeah, it's good.
[26:32.000 --> 26:33.000] Because this is automation eventually.
[26:33.000 --> 26:37.000] And if you want to automate, if you want to automate your complex processes,
[26:37.000 --> 26:39.000] then you need to learn that stuff.
[26:39.000 --> 26:41.000] How else are you going to do it?
[26:41.000 --> 26:42.000] Yeah, I agree.
[26:42.000 --> 26:46.000] I mean, that's really what, what, what Lambda step functions are is their
[26:46.000 --> 26:47.000] automation tools.
[26:47.000 --> 26:49.000] So that's probably the better way to describe it.
[26:49.000 --> 26:52.000] That's a very good point you bring up.
[26:52.000 --> 26:57.000] Another technology that I think is an emerging technology is the
[26:57.000 --> 26:58.000] managed file system.
[26:58.000 --> 27:05.000] And the reason why I think it's interesting is that, so I 20 plus years
[27:05.000 --> 27:11.000] ago, I was using file systems in the university setting when I was at
[27:11.000 --> 27:14.000] Caltech and then also in film, film industry.
[27:14.000 --> 27:22.000] So film has been using managed file servers with parallel processing
[27:22.000 --> 27:24.000] farms for a long time.
[27:24.000 --> 27:27.000] I don't know how many people know this, but in the film industry,
[27:27.000 --> 27:32.000] the, the, the architecture, even from like 2000 was there's a very
[27:32.000 --> 27:38.000] expensive file server and then there's let's say 40,000 machines or 40,000
[27:38.000 --> 27:39.000] cores.
[27:39.000 --> 27:40.000] And that's, that's it.
[27:40.000 --> 27:41.000] That's the architecture.
[27:41.000 --> 27:46.000] And now what's interesting is I see with data science and machine learning
[27:46.000 --> 27:52.000] operations that like that, that could potentially happen in the future is
[27:52.000 --> 27:57.000] actually a managed NFS mount point with maybe Kubernetes or something like
[27:57.000 --> 27:58.000] that.
[27:58.000 --> 28:01.000] Do you see any of that on the horizon?
[28:01.000 --> 28:04.000] Oh, that's a good question.
[28:04.000 --> 28:08.000] I think for our, for our, what we're currently doing, that's probably a
[28:08.000 --> 28:10.000] bit further away.
[28:10.000 --> 28:15.000] But in principle, I could very well imagine that in our use case, not,
[28:15.000 --> 28:17.000] not quite.
[28:17.000 --> 28:20.000] But in principle, definitely.
[28:20.000 --> 28:26.000] And then maybe a third, a third emerging thing I'm seeing is what's going
[28:26.000 --> 28:29.000] on with open AI and hugging face.
[28:29.000 --> 28:34.000] And that has the potential, but maybe to change the game a little bit,
[28:34.000 --> 28:38.000] especially with hugging face, I think, although both of them, I mean,
[28:38.000 --> 28:43.000] there is that, you know, in the case of pre trained models, here's a
[28:43.000 --> 28:48.000] perfect example is that an organization may have, you know, maybe they're
[28:48.000 --> 28:53.000] using AWS even for this, they're transcribing videos and they're going
[28:53.000 --> 28:56.000] to do something with them, maybe they're going to detect, I don't know,
[28:56.000 --> 29:02.000] like, you know, if you recorded customers in your, I'm just brainstorm,
[29:02.000 --> 29:05.000] I'm not seeing your company did this, but I'm just creating a hypothetical
[29:05.000 --> 29:09.000] situation that they recorded, you know, customer talking and then they,
[29:09.000 --> 29:12.000] they transcribe it to text and then run some kind of a, you know,
[29:12.000 --> 29:15.000] criminal detection feature or something like that.
[29:15.000 --> 29:19.000] Like they could build their own models or they could download the thing
[29:19.000 --> 29:23.000] that was released two days ago or a day ago from open AI that transcribes
[29:23.000 --> 29:29.000] things, you know, and then, and then turn that transcribe text into
[29:29.000 --> 29:34.000] hugging face, some other model that summarizes it and then you could
[29:34.000 --> 29:38.000] feed that into a system. So it's, what is, what is your, what are your
[29:38.000 --> 29:42.000] thoughts around some of these pre trained models and is your, are you
[29:42.000 --> 29:48.000] thinking of in terms of your stack, trying to look into doing fine tuning?
[29:48.000 --> 29:53.000] Yeah, so I think pre trained models and especially the way that hugging face,
[29:53.000 --> 29:57.000] I think really revolutionized the space in terms of really kind of
[29:57.000 --> 30:02.000] platformizing the entire business around or the entire market around
[30:02.000 --> 30:07.000] pre trained models. I think that is really quite incredible and I think
[30:07.000 --> 30:10.000] really for the ecosystem a changing way how to do things.
[30:10.000 --> 30:16.000] And I believe that looking at the, the costs of training large models
[30:16.000 --> 30:19.000] and looking at the fact that many organizations are not able to do it
[30:19.000 --> 30:23.000] for, because of massive costs or because of lack of data.
[30:23.000 --> 30:29.000] I think this is a, this is a clear, makes it very clear how important
[30:29.000 --> 30:33.000] such platforms are, how important sharing of pre trained models actually is.
[30:33.000 --> 30:37.000] I believe it's a, we are only at the, quite at the beginning actually of that.
[30:37.000 --> 30:42.000] And I think we're going to see that nowadays you see it mostly when it
[30:42.000 --> 30:47.000] comes to fairly generalized data format, images, potentially videos, text,
[30:47.000 --> 30:52.000] speech, these things. But I believe that we're going to see more marketplace
[30:52.000 --> 30:57.000] approaches when it comes to pre trained models in a lot more industries
[30:57.000 --> 31:01.000] and in a lot more, in a lot more use cases where data is to some degree
[31:01.000 --> 31:05.000] standardized. Also when you think about, when you think about banking,
[31:05.000 --> 31:10.000] for example, right? When you think about transactions to some extent,
[31:10.000 --> 31:14.000] transaction, transaction data always looks the same, kind of at least at
[31:14.000 --> 31:17.000] every bank. Of course you might need to do some mapping here and there,
[31:17.000 --> 31:22.000] but also there is a lot of power in it. But because simply also thinking
[31:22.000 --> 31:28.000] about sharing data is always a difficult thing, especially in Europe.
[31:28.000 --> 31:32.000] Sharing data between organizations is incredibly difficult legally.
[31:32.000 --> 31:36.000] It's difficult. Sharing models is a different thing, right?
[31:36.000 --> 31:40.000] Basically, similar to the concept of federated learning. Sharing models
[31:40.000 --> 31:44.000] is significantly easier legally than actually sharing data.
[31:44.000 --> 31:48.000] And then applying these models, fine tuning them and so on.
[31:48.000 --> 31:52.000] Yeah, I mean, I could just imagine. I really don't know much about
[31:52.000 --> 31:56.000] banking transactions, but I would imagine there could be several
[31:56.000 --> 32:01.000] kinds of transactions that are very normal. And then there's some
[32:01.000 --> 32:06.000] transactions, like if you're making every single second,
[32:06.000 --> 32:11.000] you're transferring a lot of money. And it happens just
[32:11.000 --> 32:14.000] very quickly. It's like, wait, why are you doing this? Why are you transferring money
[32:14.000 --> 32:20.000] constantly? What's going on? Or the huge sum of money only
[32:20.000 --> 32:24.000] involves three different points in the network. Over and over again,
[32:24.000 --> 32:29.000] just these three points are constantly... And so once you've developed
[32:29.000 --> 32:33.000] a model that is anomaly detection, then
[32:33.000 --> 32:37.000] yeah, why would you need to develop another one? I mean, somebody already did it.
[32:37.000 --> 32:41.000] Exactly. Yes, absolutely, absolutely. And that's
[32:41.000 --> 32:45.000] definitely... That's encoded knowledge, encoded information in terms of the model,
[32:45.000 --> 32:49.000] which is not personally... Well, abstracts away from
[32:49.000 --> 32:53.000] but personally identifiable data. And that's really the power. That is something
[32:53.000 --> 32:57.000] that, yeah, as I've said before, you can share significantly easier and you can
[32:57.000 --> 33:03.000] apply to your use cases. The kind of related to this in
[33:03.000 --> 33:09.000] terms of upcoming technologies is, I think, dealing more with graphs.
[33:09.000 --> 33:13.000] And so is that something from a stackwise that your
[33:13.000 --> 33:19.000] company's investigated resource can do? Yeah, so when you think about
[33:19.000 --> 33:23.000] transactions, bank transactions, right? And bank customers.
[33:23.000 --> 33:27.000] So in our case, again, it's a... We only have pseudonymized
[33:27.000 --> 33:31.000] transaction data, so actually we cannot see anything, right? We cannot see names, we cannot see
[33:31.000 --> 33:35.000] iPads or whatever. We really can't see much. But
[33:35.000 --> 33:39.000] you can look at transactions moving between
[33:39.000 --> 33:43.000] different entities, between different accounts. You can look at that
[33:43.000 --> 33:47.000] as a network, as a graph. And that's also what we very frequently do.
[33:47.000 --> 33:51.000] You have your nodes in your network, these are your accounts
[33:51.000 --> 33:55.000] or your presence, even. And the actual edges between them,
[33:55.000 --> 33:59.000] that's what your transactions are. So you have this
[33:59.000 --> 34:03.000] massive graph, actually, that also we as TMNL, as Transaction Montenegro,
[34:03.000 --> 34:07.000] are sitting on. We're actually sitting on a massive transaction graph.
[34:07.000 --> 34:11.000] So yeah, absolutely. For us, doing analysis on top of
[34:11.000 --> 34:15.000] that graph, building models on top of that graph is a quite important
[34:15.000 --> 34:19.000] thing. And like I taught a class
[34:19.000 --> 34:23.000] a few years ago at Berkeley where we had to
[34:23.000 --> 34:27.000] cover graph databases a little bit. And I
[34:27.000 --> 34:31.000] really didn't know that much about graph databases, although I did use one actually
[34:31.000 --> 34:35.000] at one company I was at. But one of the things I learned in teaching that
[34:35.000 --> 34:39.000] class was about the descriptive statistics
[34:39.000 --> 34:43.000] of a graph network. And it
[34:43.000 --> 34:47.000] is actually pretty interesting, because I think most of the time everyone talks about
[34:47.000 --> 34:51.000] median and max min and standard deviation and everything.
[34:51.000 --> 34:55.000] But then with a graph, there's things like centrality
[34:55.000 --> 34:59.000] and I forget all the terms off the top of my head, but you can see
[34:59.000 --> 35:03.000] if there's a node in the network that's
[35:03.000 --> 35:07.000] everybody's interacting with. Absolutely. You can identify communities
[35:07.000 --> 35:11.000] of people moving around a lot of money all the time. For example,
[35:11.000 --> 35:15.000] you can detect different metric features eventually
[35:15.000 --> 35:19.000] doing computations on your graph and then plugging in some model.
[35:19.000 --> 35:23.000] Often it's feature engineering. You're computing between the centrality scores
[35:23.000 --> 35:27.000] across your graph or your different entities. And then
[35:27.000 --> 35:31.000] you're building your features actually. And then you're plugging in some
[35:31.000 --> 35:35.000] model in the end. If you do classic machine learning, so to say
[35:35.000 --> 35:39.000] if you do graph deep learning, of course that's a bit different.
[35:39.000 --> 35:43.000] So basically that could for people that are analyzing
[35:43.000 --> 35:47.000] essentially networks of people or networks, then
[35:47.000 --> 35:51.000] basically a graph database would be step one is
[35:51.000 --> 35:55.000] generate the features which could be centrality.
[35:55.000 --> 35:59.000] There's a score and then you then go and train
[35:59.000 --> 36:03.000] the model based on that descriptive statistic.
[36:03.000 --> 36:07.000] Exactly. So one way how you could think about it is
[36:07.000 --> 36:11.000] whether we need a graph database or not, that always depends on your specific use case
[36:11.000 --> 36:15.000] and what database. We're actually also running
[36:15.000 --> 36:19.000] that using Spark. You have graph frames, you have
[36:19.000 --> 36:23.000] graph X actually. So really stuff in Spark built for
[36:23.000 --> 36:27.000] doing analysis on graphs.
[36:27.000 --> 36:31.000] And then what you usually do is exactly what you said. You are trying
[36:31.000 --> 36:35.000] to build features based on that graph.
[36:35.000 --> 36:39.000] Based on the attributes of the nodes and the attributes on the edges and so on.
[36:39.000 --> 36:43.000] And so I guess in terms of graph databases right
[36:43.000 --> 36:47.000] now, it sounds like maybe the three
[36:47.000 --> 36:51.000] main players maybe are there's Neo4j which
[36:51.000 --> 36:55.000] has been around for a long time. There's I guess Spark
[36:55.000 --> 36:59.000] and then there's also, I forgot what the one is called for AWS
[36:59.000 --> 37:03.000] is it? Neptune, that's Neptune.
[37:03.000 --> 37:07.000] Have you played with all three of those and did you
[37:07.000 --> 37:11.000] like Neptune? Neptune was something we, Spark of course we actually currently
[37:11.000 --> 37:15.000] using for exactly that. Also because it allows us to do
[37:15.000 --> 37:19.000] to keep our stack fairly homogeneous. We did
[37:19.000 --> 37:23.000] also PUC in Neptune a while ago already
[37:23.000 --> 37:27.000] and well Neptune you definitely have essentially two ways
[37:27.000 --> 37:31.000] how to query Neptune either using Gremlin or SparkQL.
[37:31.000 --> 37:35.000] So that means the people, your data science
[37:35.000 --> 37:39.000] need to get familiar with that which then is already one bit of a hurdle
[37:39.000 --> 37:43.000] because usually data scientists are not familiar with either.
[37:43.000 --> 37:47.000] But also what we found with Neptune
[37:47.000 --> 37:51.000] is also that it's not necessarily built for
[37:51.000 --> 37:55.000] as an analytics graph database. It's not necessarily made for
[37:55.000 --> 37:59.000] that. And that then become, then it's sometimes, at least
[37:59.000 --> 38:03.000] for us, it has become quite complicated to handle different performance considerations
[38:03.000 --> 38:07.000] when you actually do fairly complex queries across that graph.
[38:07.000 --> 38:11.000] Yeah, so you're bringing up like a point which
[38:11.000 --> 38:15.000] happens a lot in my experience with
[38:15.000 --> 38:19.000] technology is that sometimes
[38:19.000 --> 38:23.000] the purity of the solution becomes the problem
[38:23.000 --> 38:27.000] where even though Spark isn't necessarily
[38:27.000 --> 38:31.000] designed to be a graph database system, the fact is
[38:31.000 --> 38:35.000] people in your company are already using it. So
[38:35.000 --> 38:39.000] if you just turn on that feature now you can use it and it's not like
[38:39.000 --> 38:43.000] this huge technical undertaking and retraining effort.
[38:43.000 --> 38:47.000] So even if it's not as good, if it works, then that's probably
[38:47.000 --> 38:51.000] the solution your company will use versus I agree with you like a lot of times
[38:51.000 --> 38:55.000] even if a solution like Neo4j is a pretty good example of
[38:55.000 --> 38:59.000] it's an interesting product but
[38:59.000 --> 39:03.000] you already have all these other products like do you really want to introduce yet
[39:03.000 --> 39:07.000] another product into your stack. Yeah, because eventually
[39:07.000 --> 39:11.000] it all comes with an overhead of course introducing it. That is one thing
[39:11.000 --> 39:15.000] it requires someone to maintain it even if it's a
[39:15.000 --> 39:19.000] managed service. Somebody needs to actually own it and look after it
[39:19.000 --> 39:23.000] and then as you said you need to retrain people to also use it effectively.
[39:23.000 --> 39:27.000] So it comes at significant cost and that is really
[39:27.000 --> 39:31.000] something that I believe should be quite critically
[39:31.000 --> 39:35.000] assessed. What is really the game you have? How far can you go with
[39:35.000 --> 39:39.000] your current tooling and then eventually make
[39:39.000 --> 39:43.000] that decision. At least personally I'm really
[39:43.000 --> 39:47.000] not a fan of thinking tooling first
[39:47.000 --> 39:51.000] but personally I really believe in looking at your organization, looking at the people
[39:51.000 --> 39:55.000] what skills are there, looking at how effective
[39:55.000 --> 39:59.000] are these people actually performing certain activities and processes
[39:59.000 --> 40:03.000] and then carefully thinking about what really makes sense
[40:03.000 --> 40:07.000] because it's one thing but people need to
[40:07.000 --> 40:11.000] adopt and use the tooling and eventually it should really speed them up and improve
[40:11.000 --> 40:15.000] how they develop. Yeah, I think it's very
[40:15.000 --> 40:19.000] that's great advice that it's hard to understand how good of advice it is
[40:19.000 --> 40:23.000] because it takes experience getting burned
[40:23.000 --> 40:27.000] creating new technology. I've
[40:27.000 --> 40:31.000] had experiences before where
[40:31.000 --> 40:35.000] one of the mistakes I've made was putting too many different technologies in an organization
[40:35.000 --> 40:39.000] and the problem is once you get enough complexity
[40:39.000 --> 40:43.000] it can really explode and then
[40:43.000 --> 40:47.000] this is the part that really gets scary is that
[40:47.000 --> 40:51.000] let's take Spark for example. How hard is it to hire somebody that knows Spark? Pretty easy
[40:51.000 --> 40:55.000] how hard is it going to be to hire somebody that knows
[40:55.000 --> 40:59.000] Spark and then hire another person that knows the gremlin query
[40:59.000 --> 41:03.000] language for Neptune, then hire another person that knows Kubernetes
[41:03.000 --> 41:07.000] then tire another, after a while if you have so many different kinds of tools
[41:07.000 --> 41:11.000] you have to hire so many different kinds of people that all
[41:11.000 --> 41:15.000] productivity goes to a stop. So it's the hiring as well
[41:15.000 --> 41:19.000] Absolutely, I mean it's virtually impossible
[41:19.000 --> 41:23.000] to find someone who is really well versed with gremlin for example
[41:23.000 --> 41:27.000] it's incredibly hard and I think tech hiring is hard
[41:27.000 --> 41:31.000] by itself already
[41:31.000 --> 41:35.000] so you really need to think about what can I hire for as well
[41:35.000 --> 41:39.000] what expertise can I realistically build up?
[41:39.000 --> 41:43.000] So that's why I think AWS
[41:43.000 --> 41:47.000] even with some of the limitations about the ML platform
[41:47.000 --> 41:51.000] the advantages of using AWS is that
[41:51.000 --> 41:55.000] you have a huge audience of people to hire from and then the same thing like
[41:55.000 --> 41:59.000] Spark, there's a lot of things I don't like about Spark but a lot of people
[41:59.000 --> 42:03.000] use Spark and so if you use AWS and you use Spark
[42:03.000 --> 42:07.000] let's say those two which you are then you're going to have a much easier time
[42:07.000 --> 42:11.000] hiring people, you're going to have a much easier time training people
[42:11.000 --> 42:15.000] there's tons of documentation about it so I think a lot of people
[42:15.000 --> 42:19.000] are very wise that you're thinking that way but a lot of people don't think about that
[42:19.000 --> 42:23.000] they're like oh I've got to use the latest, greatest stuff and this and this and this
[42:23.000 --> 42:27.000] and then their company starts to get into trouble because they can't hire
[42:27.000 --> 42:31.000] people, they can't maintain systems and then productivity starts to
[42:31.000 --> 42:35.000] to degrees. Also something
[42:35.000 --> 42:39.000] not to ignore is the cognitive load you put on a team
[42:39.000 --> 42:43.000] that needs to manage a broad range of very different
[42:43.000 --> 42:47.000] tools or services. It also puts incredible
[42:47.000 --> 42:51.000] cognitive load on that team and you suddenly also need an incredible breadth
[42:51.000 --> 42:55.000] of expertise in that team and that means you're also going
[42:55.000 --> 42:59.000] to create single points of failures if you don't really
[42:59.000 --> 43:03.000] scale up your team.
[43:03.000 --> 43:07.000] It's something to really, I think when you go for
[43:07.000 --> 43:11.000] new tooling you should really look at it from a holistic perspective
[43:11.000 --> 43:15.000] not only about this is the latest and greatest.
[43:15.000 --> 43:19.000] In terms of Europe versus
[43:19.000 --> 43:23.000] US, have you spent much time in the US at all?
[43:23.000 --> 43:27.000] Not at all actually, flying to the US Monday but no, not at all.
[43:27.000 --> 43:31.000] That also would be kind of an interesting
[43:31.000 --> 43:35.000] comparison in that the culture of the United States
[43:35.000 --> 43:39.000] is really this culture of
[43:39.000 --> 43:43.000] I would say more like survival of the fittest or you work
[43:43.000 --> 43:47.000] seven days a week and you're constantly like you don't go on vacation
[43:47.000 --> 43:51.000] and you're proud of it and I think it's not
[43:51.000 --> 43:55.000] a good culture. I'm not saying that's a good thing, I think it's a bad
[43:55.000 --> 43:59.000] thing and that a lot of times the critique people have
[43:59.000 --> 44:03.000] about Europe is like oh will people take vacation all the time and all this
[44:03.000 --> 44:07.000] and as someone who has spent time in both I would say
[44:07.000 --> 44:11.000] yes that's a better approach. A better approach is that people
[44:11.000 --> 44:15.000] should feel relaxed because when
[44:15.000 --> 44:19.000] especially the kind of work you do in MLOPs
[44:19.000 --> 44:23.000] is that you need people to feel comfortable and happy
[44:23.000 --> 44:27.000] and more the question
[44:27.000 --> 44:31.000] what I was going to is that
[44:31.000 --> 44:35.000] I wonder if there is a more productive culture
[44:35.000 --> 44:39.000] for MLOPs in Europe
[44:39.000 --> 44:43.000] versus the US in terms of maintaining
[44:43.000 --> 44:47.000] systems and building software where the US
[44:47.000 --> 44:51.000] what it's really been good at I guess is kind of coming up with new
[44:51.000 --> 44:55.000] ideas and there's lots of new services that get generated but
[44:55.000 --> 44:59.000] the quality and longevity
[44:59.000 --> 45:03.000] is not necessarily the same where I could see
[45:03.000 --> 45:07.000] in the stuff we just talked about which is if you're trying to build a team
[45:07.000 --> 45:11.000] where there's low turnover
[45:11.000 --> 45:15.000] you have very high quality output
[45:15.000 --> 45:19.000] it seems like that maybe organizations
[45:19.000 --> 45:23.000] could learn from the European approach to building
[45:23.000 --> 45:27.000] and maintaining systems for MLOPs.
[45:27.000 --> 45:31.000] I think there's definitely some truth in it especially when you look at the median
[45:31.000 --> 45:35.000] tenure of a tech person in an organization
[45:35.000 --> 45:39.000] I think that is actually still significantly lower in the US
[45:39.000 --> 45:43.000] I'm not sure I think in the Bay Area somewhere around one year or two months or something like that
[45:43.000 --> 45:47.000] compared to Europe I believe
[45:47.000 --> 45:51.000] still fairly low. Here of course in tech people also like to switch companies more often
[45:51.000 --> 45:55.000] but I would say average is still more around
[45:55.000 --> 45:59.000] two years something around that staying with the same company
[45:59.000 --> 46:03.000] also in tech which I think is a bit longer
[46:03.000 --> 46:07.000] than you would typically have it in the US.
[46:07.000 --> 46:11.000] I think from my perspective where I've also built up most of the
[46:11.000 --> 46:15.000] current team I think it's
[46:15.000 --> 46:19.000] super important to hire good people
[46:19.000 --> 46:23.000] and people that fit to the team fit to the company culture wise
[46:23.000 --> 46:27.000] but also give them
[46:27.000 --> 46:31.000] let them not be in a sprint all the time
[46:31.000 --> 46:35.000] it's about having a sustainable way of working in my opinion
[46:35.000 --> 46:39.000] and that sustainable way means you should definitely take your vacation
[46:39.000 --> 46:43.000] and I think usually in Europe we have quite generous
[46:43.000 --> 46:47.000] even by law vacation I mean in Netherlands by law you get 20 days a year
[46:47.000 --> 46:51.000] but most companies give you 25 many IT companies
[46:51.000 --> 46:55.000] 30 per year so that's quite nice
[46:55.000 --> 46:59.000] but I do take that so culture wise it's really everyone
[46:59.000 --> 47:03.000] likes to take vacations whether that's sea level or whether that's an engineer on a team
[47:03.000 --> 47:07.000] and that's in many companies that's also really encouraged
[47:07.000 --> 47:11.000] to have a healthy work life balance
[47:11.000 --> 47:15.000] and of course it's not only about vacations also but growth opportunities
[47:15.000 --> 47:19.000] letting people explore develop themselves
[47:19.000 --> 47:23.000] and not always pushing on max performance
[47:23.000 --> 47:27.000] so really at least I always see like a partnership
[47:27.000 --> 47:31.000] the organization wants to get something from an
[47:31.000 --> 47:35.000] employee but the employee should also be encouraged and developed
[47:35.000 --> 47:39.000] in that organization and I think that is something that in many parts of
[47:39.000 --> 47:43.000] Europe where there is big awareness for that
[47:43.000 --> 47:47.000] so my hypothesis is that
[47:47.000 --> 47:51.000] it's possible that Europe becomes
[47:51.000 --> 47:55.000] the new hub of technology
[47:55.000 --> 47:59.000] and I'll tell you why here's my hypothesis the reason why is that
[47:59.000 --> 48:03.000] in terms of machine learning operations
[48:03.000 --> 48:07.000] I've already talked to multiple people who know the
[48:07.000 --> 48:11.000] data around it like big companies and they've told me that
[48:11.000 --> 48:15.000] it's going to be close to impossible to hire people soon
[48:15.000 --> 48:19.000] because essentially there's too many job openings
[48:19.000 --> 48:23.000] and there's not enough people that know machine learning, machine learning operations, cloud computing
[48:23.000 --> 48:27.000] and so the American culture unfortunately I think
[48:27.000 --> 48:31.000] is so cutthroat that they don't encourage
[48:31.000 --> 48:35.000] people to be loyal to their company
[48:35.000 --> 48:39.000] and in addition to that because there is no universal healthcare system
[48:39.000 --> 48:43.000] in the US
[48:43.000 --> 48:47.000] it's kind of a prisoner's dilemma where nobody
[48:47.000 --> 48:51.000] sees each other and so they're constantly optimizing
[48:51.000 --> 48:55.000] but in the case of machine learning it's a different
[48:55.000 --> 48:59.000] industry where you do really need to have
[48:59.000 --> 49:03.000] some longevity for employees because the systems are very complex
[49:03.000 --> 49:07.000] system to develop and so if the culture of Europe
[49:07.000 --> 49:11.000] which is much more friendly to the worker I think it
[49:11.000 --> 49:15.000] could lead to Europe having
[49:15.000 --> 49:19.000] a better outcome for machine learning operations
[49:19.000 --> 49:23.000] so that's one part of it and then the second part of it is the other thing the US has
[49:23.000 --> 49:27.000] has done that I think Europe
[49:27.000 --> 49:31.000] has done that if I compare Europe versus the US in terms of
[49:31.000 --> 49:35.000] data privacy that I think the US has dropped the ball
[49:35.000 --> 49:39.000] and they haven't done a good job at it but Europe has actually
[49:39.000 --> 49:43.000] done much much better at holding tech companies accountable
[49:43.000 --> 49:47.000] and I think if you asked
[49:47.000 --> 49:51.000] well informed people if they would like some of the
[49:51.000 --> 49:55.000] practices of the United States tech companies to change I think most
[49:55.000 --> 49:59.000] well informed people would say we don't want you to recommend
[49:59.000 --> 50:03.000] bad data like extremist video content
[50:03.000 --> 50:07.000] I mean there's people that are extremists that love it
[50:07.000 --> 50:11.000] or we don't want you to sell our personal information without our consent
[50:11.000 --> 50:15.000] so it could also lead to a better
[50:15.000 --> 50:19.000] outcome for the people
[50:19.000 --> 50:23.000] that are using machine learning and AI in Europe
[50:23.000 --> 50:27.000] so I actually suspect and this is my hypothesis
[50:27.000 --> 50:31.000] who knows if I'm true or not is that I think Europe could be
[50:31.000 --> 50:35.000] the leader from let's say 2022 to
[50:35.000 --> 50:39.000] 2040 in AI and ML because of
[50:39.000 --> 50:43.000] the culture but I don't know that's just one hypothesis I have
[50:43.000 --> 50:47.000] yeah I think around the what you mentioned before
[50:47.000 --> 50:51.000] around the fact that perhaps Turnover is in tech companies here in Europe
[50:51.000 --> 50:55.000] is less I think that definitely helps you build systems that survive the test of time as well
[50:55.000 --> 50:59.000] right I mean everyone had the case when a key engineer
[50:59.000 --> 51:03.000] off boards from a team leaves the company and then you need to
[51:03.000 --> 51:07.000] hire another person right it's long times of not being super productive
[51:07.000 --> 51:11.000] long time not being super effective so you continuously
[51:11.000 --> 51:15.000] lose track that you need
[51:15.000 --> 51:19.000] so I think you could be right there that in the
[51:19.000 --> 51:23.000] longer run when systems really need to be matured and developed over
[51:23.000 --> 51:27.000] longer time Europe might have an edge there
[51:27.000 --> 51:31.000] might be a bit better suited to do that
[51:31.000 --> 51:37.000] the salaries are still higher in the US and also I think many US companies are starting to enter more
[51:37.000 --> 51:41.000] from a people perspective even remote work and everything they're starting to also
[51:41.000 --> 51:45.000] poach more and more engineers from Europe because
[51:45.000 --> 51:49.000] of course vacation and everything and having a healthy work life balance
[51:49.000 --> 51:53.000] is one thing but for many people if you
[51:53.000 --> 51:57.000] give you a 50% higher paycheck that's also a strong argument
[51:57.000 --> 52:01.000] so it's difficult actually to also for Europe to
[52:01.000 --> 52:05.000] keep the engineers here that as well
[52:05.000 --> 52:09.000] no I will say this though if you work remote from
[52:09.000 --> 52:13.000] Europe that's a very different scenario than living
[52:13.000 --> 52:17.000] in the US because you'll see when
[52:17.000 --> 52:21.000] unfortunately the United States since about 1980
[52:21.000 --> 52:25.000] has declined and
[52:25.000 --> 52:29.000] the data around the US is pretty dire
[52:29.000 --> 52:33.000] actually the life expectancy is one of the
[52:33.000 --> 52:37.000] lowest in the world for a G20 country
[52:37.000 --> 52:41.000] so then if you walk through the major
[52:41.000 --> 52:45.000] cities of the US there's just poverty
[52:45.000 --> 52:49.000] everywhere like people are living in very low
[52:49.000 --> 52:53.000] quality conditions where every time I go to Europe
[52:53.000 --> 52:57.000] I go to Munich, I go to London, I go to wherever
[52:57.000 --> 53:01.000] that basically the cities are beautiful
[53:01.000 --> 53:05.000] and well maintained so I think if the cases that if a US company
[53:05.000 --> 53:09.000] let a European live in Europe and work
[53:09.000 --> 53:13.000] remote yeah that could work out because the European
[53:13.000 --> 53:17.000] citizen has an EU citizen has amazing
[53:17.000 --> 53:21.000] healthcare they have the
[53:21.000 --> 53:25.000] safety net their cities aren't basically
[53:25.000 --> 53:29.000] highly unequal but I think it's the
[53:29.000 --> 53:33.000] location of the US in its current form
[53:33.000 --> 53:37.000] I personally wouldn't recommend
[53:37.000 --> 53:41.000] someone from Europe moving to the US because
[53:41.000 --> 53:45.000] unfortunately I think it's a
[53:45.000 --> 53:49.000] great place to live just to be totally honest
[53:49.000 --> 53:53.000] if you're already in Europe and on the flip side I think that
[53:53.000 --> 53:57.000] there's a lot of Americans actually who are very interested in
[53:57.000 --> 54:01.000] universal healthcare in particular is not even
[54:01.000 --> 54:05.000] possible in the US because of the politics in the US
[54:05.000 --> 54:09.000] and a lot of medical bankruptcies occur
[54:09.000 --> 54:13.000] and so from a start up perspective as well
[54:13.000 --> 54:17.000] this is something that people don't talk about in America it's like yeah we're all about
[54:17.000 --> 54:21.000] startups well think about how many more people would be able to
[54:21.000 --> 54:25.000] create a company if you didn't have to worry about going bankrupt
[54:25.000 --> 54:29.000] if you broke your arm or you have some kind of
[54:29.000 --> 54:33.000] sickness or whatever so
[54:33.000 --> 54:37.000] I think it's an interesting trade off
[54:37.000 --> 54:41.000] situation and I would say that the sweet spot might be
[54:41.000 --> 54:45.000] you work for an American company and get the higher salary but you still live in Europe
[54:45.000 --> 54:49.000] that would be the dream scenario I think that's why many people are actually doing it
[54:49.000 --> 54:53.000] I think especially since covid started you can really see it
[54:53.000 --> 54:57.000] before that it wasn't really a thing working for a US company
[54:57.000 --> 55:01.000] who really sits in the US and you're full remote but I think now since 2, 2 and a half years
[55:01.000 --> 55:05.000] it's really becoming reality actually
[55:05.000 --> 55:09.000] interesting yeah well
[55:09.000 --> 55:13.000] hearing a lot of your ideas around
[55:13.000 --> 55:17.000] startups and what you're doing and
[55:17.000 --> 55:21.000] also about how you're a SageMaker
[55:21.000 --> 55:25.000] is there any place that someone can get a hold of you
[55:25.000 --> 55:29.000] if they listen to this on the Orelia platform or
[55:29.000 --> 55:33.000] think content that you're developing yourself or any other information you want to share
[55:33.000 --> 55:37.000] yeah definitely so I think best place to reach out to me and I'm always
[55:37.000 --> 55:41.000] happy to receive a few messages and have a good chat or a virtual coffee
[55:41.000 --> 55:45.000] is via LinkedIn my name is here that's how you can find me on LinkedIn
[55:45.000 --> 55:49.000] I'm also at conferences here and there well in Europe mostly
[55:49.000 --> 55:53.000] typically when there is an MLOps conference you're probably going to see me there
[55:53.000 --> 55:57.000] in one way or another that is something as well
[55:57.000 --> 56:01.000] cool yeah well I'm glad we had a chance to talk
[56:01.000 --> 56:05.000] you taught me a few things that I'm definitely going to follow up on
[56:05.000 --> 56:09.000] and I really appreciate it and hopefully we can talk again soon
[56:09.000 --> 56:13.000] thanks a lot for the chat okay all right
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
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AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
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O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
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Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
O'Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform
https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Amazing career advice and deep dive into Hugging Face with Julien Simon Chief Evangelist at Hugging Face. Connect with Julien at: https://www.linkedin.com/in/juliensimon/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity: https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Talk with Jon Reifschneider | Duke AI Master of Engineering
https://ai.meng.duke.edu/faculty/jon-reifschneider
Talk with ML Engineering and Duke MIDS Alumni Malcolm Smith Fraser about doing MLOPs pipelines on AWS for computer vision.
https://www.linkedin.com/in/malcolmsfraser/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Learn to prepare for the AWS Solutions Architect Exam
00:00 Intro
01:47 Domain 1: Design Resilient Architectures
02:32 Domain 2: Design High-Performance Architectures
03:17 Domain 3: Design Scalable Architectures and Architectures
03:33 Domain 4: Design Cost-Optimized Architectures
04:40 Exam Guide Walkthrough
09:25 Whitepapers and why they are important
10:29 Well-Architected Framework
11:08 AWS FAQ
14:30 AWS Quick Starts
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Episode 22 covers the preparing for Disasters.
00:00 Intro
02:37 Planning for failures
03:34 Avoiding and planning for disasters
05:37 Using the Well-Architected Framework design principles
05:51 Recovery Point objectives (RPO)
06:34 Recovery time objective (RTO)
07:12 Plan for disaster recovery
08:03 Storage and backup building blocks
09:50 S3 Cross-Region replication
10:41 EBS volume snapshots
11:41 File system replication
12:50 Compute Capacity recovery
13:44 Strategies for disaster recovery
15:01 Networking design for resilience
15:34 Databases and recovery
16:45 Automation Services: CloudFormation, Elastic Beanstalk and AWS OpsWorks
17:38 Four disaster recovery strategies: Backup and restore, Pilot light, Warm Standby and Multi-site
18:23 AWS Storage Gateway
24:00 Summary of common DR patterns
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
00:00 Intro
04:00 Forms of decoupling
07:00 SQS
12:00 SQS Use Cases
19:27 SNS
23:21 SNS vs SQS
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
f you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Alright, so I'm live here 52 weeks of AWS, continuing to cover the solutions architect certification material. And today I'm going to talk about securing user and application access. Probably one of the most timely topics that we can discuss for cloud computing, there is a lot of increased risk of cybersecurity threats in in the world right now. And there's conflicts that could potentially make your organization really need to care a bit more about cybersecurity. And so this is a great topic for today. So let's go ahead and dive right in here. I'm going to talk through this material on securing user and application access. I'm going to go ahead and share my screen if you're watching. Live here with the video, and let's get to it. Okay, so first up securing user application access. We're talking about some of the things like architectural needs the user account and I ns, how to organize users do federated users multiple accounts. also play around a little bit with AWS itself and do some demos, if it seems like it's needed. So by the end of this talk, today, I'm going to cover I am groups roles, how to use user Federation, also about AWS organizations, and how to manage multiple AWS accounts, which is, in fact, a really good process for many organizations. Okay, let's get into architectural need first. So, you know, that's typically a good place to start as what's the structure of your company, what it is you need to solve, then move into the details. So the first thing that most people don't do that they should do when they're using AWS is they need to secure the root account. I've personally seen this happen at multiple companies, where you everybody was using the root and now account initially, because it's a startup. And, you know, we want to move fast and break things or, you know, like, I like to say, move fast and break democracy. But in general, with root users, you need to secure them immediately, because it's so easy to essentially give someone access to your account. And then now you don't have a company anymore, you've given it away to other people. And the first thing to do would be to create a admin user account, the next thing to do is make sure that you lock away the root credentials, and then don't use the root account period. So instead, what you would want to do is use the admin or specific admin users, maybe an admin for s3, or an admin for compute, or something like that, for most of the tasks. So I am is a way of managing identity and access management, you can securely control individual and group access, you can integrate with other AWS services, do Federated Identity Management, granular permissions, and also MFA or multi factor authentication.
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Learn to connect networks on AWS
00:00 Intro
01:24 Module Overview
03:19 Connecting multiple VPNs
05:09 AWS Direct Connect
08:58 Connecting VPCs
11:03 VPC Peering
14:08 Transit Gateway
22:56 Module Wrap-up
Okay, I'm live here with 52 weeks of AWS episode 13. I'm still focused on the solutions architect material. And last week, I was able to talk about some networking material. But this week, I'm going to get into more networking and talk about connecting networks. And what we'll do is I'll just go ahead and share my screen here to start with. And let's go ahead and get started. Go here connecting. And here we go. Got the material up here connecting networks. Let's go to a presenter view. So a few things to talk about with connecting network. We're going to cover these topics today. Architectural needs connecting to a site to site VPN. Also talking about direct connect VPC, in AWS with VPC peering. This happens a lot with maybe a SaaS offering that you're using, like Databricks, scaling your VPC network, with transit gateway, and then also connecting to VPC via supported services.
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Shubham (LI: https://www.linkedin.com/in/shubhamsaboo, Twitter: https://twitter.com/Saboo_Shubham_)
Sandra (LI: https://www.linkedin.com/in/sandrakublik, Twitter: https://twitter.com/sandra_kublik)
Kairos Data Labs (LI: https://www.linkedin.com/company/kairos-data-labs, Youtube: https://www.youtube.com/channel/UCWRXc4CeXy5f0dQdJ2XWliw)
Read GPT-3 Book Here: https://learning.oreilly.com/library/view/gpt-3/9781098113612/
Buy GPT-3 Book Here: https://www.amazon.com/GPT-3-Building-Innovative-Products-Language/dp/1098113624/ref=sr_1_2?crid=3B7EBW0BGWJGS&keywords=gpt-3+book&qid=1645194541&sprefix=gpt-3+book%2Caps%2C48&sr=8-2
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Part 1 of solutions architect cert
00:00 Intro 01:30 Overview of Exam 07:00 AWS Architecture 08:00 Well Architected 11:00 Scalability 17:00 AWS Regions 22:00 AWS S3 26:00 AWS Costs 28:00 AWS Snowball and Snowmobile
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
aws-cdk-python-helloHello World Python CDK
In this episode I dive into IAC with AWS Lambda on AWS Cloud9
00:00 Intro
02:00 Overview of IAC Architecture
05:00 Setup AWS Cloud9
06:57 Read CDK Project Setup
08:00 Upgrade CDK
12:33 Create AWS Lambda in Python Marco/Polo Function
14:08 Setup CDK Stack
15:32 Deploy Changes via CDK Deploy
19:32 Invoke Lambda via CDK
21:15 Invoke Lambda via Cloud9
Reference* Hello Python CDK Workshop * Watch on O'Reilly: AWS CDK with Python Deploy Hello World Lambda * Watch on YouTube
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
zero-to-five-aws-bootcamp
A Graduate Level Three to Five Week Bootcamp on AWS. Go from ZERO to FIVE Certifications.
Week 1: AWS Certified Solutions Architect & Cloud Practicioner
Resources
Slides and Links
Video Content
Week 2: AWS Certified Data Analytics & AWS Developer
Resources
Slides and Links
Video Content
Week 3: AWS Certified Machine Learning
Resources
Slides and Links
Video Content
Week 4: AWS Solutions Architect Pro
Resources
Slides and Links
Video Content
52 Weeks of AWS Podcast and Live Stream
Livestream every Tuesday at 3pm ET on YouTube/Linkedin/Twitch.
Episode 1:O'Reilly C# on AWS book overview
Episode 2: Reinvent Recap and Getting Started with AWS
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Part2: Talk about getting started:
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Part 3: Cloud development environments
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AWS Cloudshell Can run Bash, ZSH or Powershell
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AWS Cloud9 Supports many languages including Python and C#
Episode 3: AWS CP Part 1
Episode 4: AWS CP Part 2
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Benchmarking: https://github.com/noahgift/benchmarking-aws
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History of AWS (AWS Shareholder Letter 2020): https://www.aboutamazon.com/news/company-news/2020-letter-to-shareholders
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Visual Studio AWS Tool
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Github Codespaces vscode tutorial: https://github.com/noahgift/DotNet-AWS/blob/main/chapters/appendix/AppendixB-CSharp-Tutorial.md
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AWS CP Part 2: Cover Global infra and security
Episode 5: AWS CP Part 3
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Writing a AWS S3 Bucket Lister application in Visual Studio 2022
-
AWS CP Part 3: Network and Content Delivery, Compute Storage
Episode 6
Potential Topics
IAC
Global Resources
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Episode 5: AWS CP Part 3
-
Writing a AWS S3 Bucket Lister application in Visual Studio 2022
-
AWS CP Part 3: Network and Content Delivery, Compute Storage
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Episode 4: AWS CP Part 2* Benchmarking: https://github.com/noahgift/benchmarking-aws * History of AWS (AWS Shareholder Letter 2020): https://www.aboutamazon.com/news/company-news/2020-letter-to-shareholders * Visual Studio AWS Tool * Github Codespaces vscode tutorial: https://github.com/noahgift/DotNet-AWS/blob/main/chapters/appendix/AppendixB-CSharp-Tutorial.md * AWS CP Part 2
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
- Part 1: Cover Reinvent 2021 announcements
- Part2: Talk about getting started:
- AWS Free Tier
- AWS Academy (for students)
- AWS Sagemaker Studio Lab
- Part 3: Cloud development environments
- AWS Cloudshell Can run Bash, ZSH or Powershell
- AWS Cloud9 Supports many languages including Python and C#
- Notes on Episode 2
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
Outline
Key Book Facts:
-
(8 chapters: 30 pages/chapter & 240-250 total length)
-
Each chapter has one more more independent code examples in Github
-
Chapter 1: Getting started with .NET on AWS
-
What is Cloud Computing
-
Types of Cloud Computing:
-
IaaS
-
PaaS, FaaS and Serverless
-
SaaS
-
MaaS
-
Key Cloud Computing Concepts
-
Elastic Infrastructure
-
Overview of Core Services
-
High-level overview of AWS
-
History of AWS
-
Global Infrastructure
-
Using AWS
-
Setting up an account
-
Using AWS Console
-
Setting up and Using IAM
-
Setting up and Developing AWS C# SDK with:
-
Quickstart of cross-platform C# app
-
AWS Cloudshell
-
AWS Cloud9
-
Visual Studio
-
Visual Studio Code and Visual Studio Codespaces on Github
-
Chapter 2: AWS Core Services
-
AWS Storage
-
Overview of AWS Storage
-
Developing with S3 Storage
-
Developing with EBS Storage
-
Using EFS Storage
-
Using EC2 Compute
-
Overview of EC2
-
Using EC2
-
Using EC2 Instance Types
-
Using EC2 Purchase Options
-
Security Best Practices for AWS
-
Encryption at REST and Transit
-
PLP (Principle of Least Privilege
-
Developing NoSQL Solutions with DynamoDB
-
What is DynamoDB
-
Key DynamoDB Concepts
-
Build a Sample C# DynamoDB Console App
-
Chapter 3: Migrating a legacy .NET application to AWS
-
Choosing a migration path
-
Rehosting
-
Replatforming
-
Repurchasing
-
Refactoring
-
Retire
-
Retain
-
Rehosting .NET Framework
-
App2Container
-
Rehosting .NET Core / 5
-
.NET Core Elastic Beanstalk
-
Replatforming: Migrating .NET Framework
-
Considerations for moving to .NET 5
-
Microsoft .NET Upgrade Assistant
-
AWS Porting Assistant for .NET
-
Migrating Build and Deploy to AWS
-
Teamcity to AWS Code Build
-
Selecting Deploy Compute Target Environment
-
Chapter 4: Modernizing .NET applications to Serverless
-
What is “Serverless” Computing?
-
Choosing the correct Serverless components for .NET on AWS
-
Developing with AWS Lambda and C#
-
Developing with AWS Step Functions
-
Developing with services with SQS and SNS
-
Developing Event Driven via AWS Triggers
-
Developing Serverless .NET Microservices on AWS
-
What is a Microservice according to AWS?
-
Overview of AWS Microservice options
-
Develop RESTful API with AWS App Runner
-
Developing RESTful API with AWS Lambda, API Gateway and SAM
-
Chapter 5: Containerization of .NET
-
Developing with Containers on AWS
-
Introduction to Containers
-
Comparing Containers to Hardware Virtualization
-
Advantages of Containers
-
Building Microservices with Containers
-
Introduction to Kubernetes
-
What is Kubernetes?
-
Understanding Kubernetes on AWS
-
Developing with AWS Container Compatible Services
-
Amazon ECR
-
Amazon ECS and Fargate
-
Amazon EKS
-
AWS App Runner
-
AWS Lambda
-
Chapter 6: DevOps
-
Getting started with DevOps on AWS?
-
What is DevOps?
-
What are AWS DevOps best practices
-
Developing with CI/CD
-
AWS Code Build
-
AWS Code Pipeline
-
Integrating 3rd party build servers
-
Jenkins
-
Teamcity
-
Github Actions
-
Developing with IAC
-
What is IAC?
-
Developing with Amazon CDK for IAC
-
What is CDK?
-
Working with CDK in C#
-
Developing with Terraform for IAC
-
Chapter 7: Monitoring, Instrumentation and Auditing and Testing for .NET
-
Using AWS Cloudwatch
-
Alarms, Logs, Metrics
-
Application monitoring
-
ServiceLens
-
Traces, Resource Health and Synthetic Canaries
-
Enabling SDK Metrics and Additional Tools
-
Using AWS Cloudtrail for Security Auditing
-
Continuous Delivery Key Concepts for .NET on SDK
-
Chapter 8: Developing with AWS C# SDK
-
Using AWS Toolkit for Visual Studio in Depth
-
Configuring Visual Studio for AWS Toolkit
-
Special Features of Visual Studio for AWS Toolkit
-
Key SDK Features
-
Async APIs
-
Retries and Timeouts
-
Paginators
-
Working with High-level AWS Services
-
Using AWS Rekognition
-
Using AWS Comprehend
-
Using AWS Sagemaker
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/