Adventures in Machine Learning: Recent Episodes

Top End Devs

Seasoned pro or complete beginner, everyone can join our weekly Adventures in Machine Learning podcast. We're covering all the breakthroughs, influencers, and resources of Machine Learning with our venturesome AI panel and guests. We discuss advanced concepts in plain English. This is the AI podcast you've been looking for.

In this episode, we dive deep into the evolving landscape of digital marketing and brand storytelling. We explore how the intersection of authenticity, community, and technology is reshaping how brands connect with people—and why it's no longer just about the product, but about the experience.

We talk about how we've shifted our focus from performance-only metrics to a more holistic approach, blending creativity with strategy. There's a big emphasis on human-first marketing—building trust, showing up consistently, and leading with values that resonate.

We also reflect on the role of content creators and influencers in today’s market, and how brands can partner more meaningfully instead of just transacting for reach. It’s about collaboration, not commodification.

Key takeaways:* Authenticity wins. Audiences can tell when it’s forced. * Content isn't king—connection is. * Brand loyalty is built through trust, not just a strong call to action. * It’s time to ditch the funnel mindset and embrace more circular, relationship-driven marketing. * Data is powerful, but gut instinct and creativity still matter—a lot.

Whether you’re a marketer, entrepreneur, or creator, there’s something in here for you. Let’s keep pushing the industry forward—together.

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Welcome back to another episode of Adventures in Machine Learning, where hosts Michael Berk and Ben Wilson delve into the intricate process of implementing model serving solutions. In this episode, they explore a detailed case study focused on enhancing search functionality with a particular emphasis on a hot dog recipe search engine. The discussion takes you through the entire development loop, beginning with understanding product requirements and success criteria, moving through prototyping and tool selection, and culminating in team collaboration and stakeholder engagement. Michael and Ben share their insights on optimizing for quick signal in design, leveraging existing tools, and ensuring service stability. If you're eager to learn about effective development strategies in machine learning projects, this episode is packed with valuable lessons and behind-the-scenes engineering perspectives. Join us as we navigate the challenges and triumphs of building impactful search solutions.

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Welcome to another insightful episode of Top End Devs, where we delve into the fascinating world of machine learning and data science. In this episode, host Charles Max Wood is joined by special guest Pierpaolo Hipolito, a data scientist at the SAS Institute in the UK. Together, they explore the intriguing paradoxes of data science, discussing how these paradoxes can impact the accuracy of machine learning models and providing insights on how to mitigate them.

Pierpaolo shares his expertise on causal reasoning in machine learning, drawing from his master's research and contributions to Towards Data Science and other notable publications. He elaborates on the complexities of data modeling during the early stages of the COVID-19 pandemic, highlighting the use of simulation and synthetic data to address data sparsity.

Throughout the conversation, the focus remains on the importance of understanding the underlying system being modeled, the role of feature engineering, and strategies for avoiding common pitfalls in data science. Whether you are a seasoned data scientist or just starting out, this episode offers valuable perspectives on enhancing the reliability and interpretability of your machine learning models.

Tune in for a deep dive into the paradoxes of data science, practical advice on feature interaction, and the importance of accurate data representation in achieving meaningful insights.

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What do cows and camels have to do with the human brain? The latest developments in machine learning, of course! In this episode, Michael and Ben dive into a new white paper from Facebook AI researchers that reveals a LOT about the future of modeling. They discuss “cows and camels”, the question of predictive vs causal modeling, and how algorithms are getting scary good at emulating the human brain these days.
In This Episode
Why Facebook’s new research is VERY exciting for AI learning and causality (but what does it have to do with cows and camels?)
The answer to “Is predictive or causal modeling more accurate?” (and why it’s not the best question to ask)
Not sure if you need machine learning or just plain data modeling? Michael lays it out for you
What algorithms are learning about human behavior to accurately emulate the human brain in 2022 and beyond

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Michael Berk joins the adventure to discuss how he uses Machine Learning within the context of A/B testing features within applications and how to know when you have a viable test option for your setup.

Links
* How to Find Weaknesses in your Machine Learning Models * LinkedIn: Michael Berk * Michael Berk - Medium

Picks
* Ben- David Thorne Books * Charles- Shadow Hunter * Michael- Stuart Russell

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In today's episode, we dive into the critical decision-making process of building versus buying technology solutions, especially when it comes to agentic logic-based frameworks. With the industry still in its early stages, I recommend waiting for managed solutions to mature, while Ben suggests the educational value of simple project builds. They discuss the importance of understanding the technology thoroughly before diving into business-focused decisions, using tools like customer user journeys (CUJs) to evaluate scalability, cost-efficiency, and maintainability. They also highlight some initial challenges and missteps in project management and the necessity for pre-evaluation by tech teams.
For non-technical teams engaged in technical projects, they provide structured guidance on navigating these unknowns efficiently. Additionally, they emphasize the value of research spikes and incremental development to manage risk and learn from user behavior. Finally, they explore the promising yet evolving landscape of generative AI and its potential high ROI with Retrieval-Augmented Generation (RAG).

Socials
* Linkedin: Ben Wilson * LinkedIn: Michael Berk

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Peter Elger and Eóin Shanaghy join Charles Max Wood to dive into what Artificial Intelligence and Machine Learning related services are available for people to use. Peter and Eóin are experts in AWS and explain what is provided in its services, but easily extrapolate to other clouds. If you're trying to implement Artificial Intelligence algorithms, you may want to use or modify an algorithm already built and provided to you.

Links
* fourTheorem * Twitter: Eóin Shanaghy * Twitter: Peter Elger

Picks
* Charles- The Eye of the World: Book One of The Wheel of Time by Robert Jordan * Charles - Changemakers With Jamie Atkinson * Charles- Podcast Domination Show by Luis Diaz * Charles- Buzzcast * Charles- Podcast Talent Coach * Eóin- IKEA | IDÅSEN Desk sit/stand, black/dark gray63x31 1/2 " * Eóin- Kinesis | Freestyle2 Split- Adjustable Keyboard for PC * Peter- The Wolfram Physics Project * Peter- PBS Space Time * Peter- Youtube Channel | 3Blue1Brown * Peter- Cracking the Code

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The Eye of the World: Book One of The Wheel of Time (Wheel of Time, 1)

In this episode, Ben and Michael explore burnout, particularly in machine learning and data science. They highlight that burnout stems from exhaustion, cynicism, and inefficiency and can be caused by repetitive tasks, overwhelming workloads, or being in the wrong role. They also tackle strategies to combat burnout, including collaborating with others, mentoring, shifting focus between tasks, and hiring more people to distribute the workload. A key takeaway is the importance of knowledge sharing and not hoarding tasks for job security, as this can lead to burnout and inefficiency. They also discuss managing burnout and its components, particularly exhaustion, cynicism, and inefficiency, through personal experiences. Finally, they talk about how burnout can lead to inefficiency and physical manifestations, like a lack of motivation to engage in activities outside of work.

Socials
* LinkedIn: Ben Wilson * LinkedIn: Michael Berk

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Rishal Hurbans is the author of Grokking Artificial Intelligence Algorithms. He walks us through how to learn different Machine Learning algorithms. He also then walks us through the different types of algorithms based on different natural systems and processes.

Links* Kaggle: Your Machine Learning and Data Science Community * Rishal Hurbans * Inktober * Book giveaway link

Picks* Chuck- Hero with a thousand faces by Joseph Campbell * Chuck- Masterbuilt smoker * Rishal-Learn something new everyday * Rishal- Building a StoryBrand by Donald Miller

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The Hero with a Thousand Faces (The Collected Works of Joseph Campbell)

Building a StoryBrand: Clarify Your Message So Customers Will Listen

Masterbuilt MB20070210 Analog Electric Smoker with 3 Smoking Racks, 30 inch, Black

In today’s episode, Michael and Ben are joined by industry expert Barzan Mozafari, the CEO and co-founder at Keebo. He delves deep into the evolving landscape of data learning and cloud optimization. They explore how understanding data distribution can lead to early detection of anomalies and how optimizing data workflows can result in significant cost savings and unintended business growth. Barzan sheds light on leveraging existing cloud technologies and the role of automated tools in enhancing system interactions, while Ben talks about the intricacies of platform migration and tech debt.
They dig into the challenges and strategies for optimizing complex data pipelines, the economic pressures faced by data teams, and insights into innovation stemming from academic research. The conversation also covers the importance of maintaining customer trust without compromising data security and the iterative nature of both academic and industrial approaches to problem-solving. Join them as they navigate the intersection of technical debt, AI-driven optimization, and the dynamic collaboration between researchers and engineers, all aimed at driving continuous improvement and innovation in the world of data.
So, gear up for an episode packed with insights on shrinking pie data learning, cloud costs, automated optimization tools, and much more. Let’s dive right in!

Socials
* LinkedIn: Barzan Mozafari

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Today, join Michael and Ben as they delve into crucial topics surrounding code security and the safe execution of machine learning models. This episode focuses on preventing accidental key leaks in notebooks, creating secure environments for code execution, and the pros and cons of various isolation methods like VMs, containers, and micro VMs.
They explore the challenges of evaluating and executing generated code, highlighting the risks of running arbitrary Python code and the importance of secure evaluation processes. Ben shares his experiences and best practices, emphasizing human evaluation and secure virtual environments to mitigate risks.
The episode also includes an in-depth discussion on developing new projects with a focus on proper engineering procedures, and the sophisticated efforts behind Databricks' Genie service and MLflow's RunLLM. Finally, Ben and Michael explore the potential of fine-tuning machine learning models, creating high-quality datasets, and the complexities of managing code execution with AI.
Tune in for all this and more as we navigate the secure pathways to responsible and effective machine learning development.

Socials
* LinkedIn: Michael Berk * LinkedIn: Ben Wilson

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They delve into the journeys and insights of distinguished leaders in the development world. In today's episode, Michael engages with Brian Vallelunga, the visionary CEO of Doppler. Brian shares his compelling journey from early tech innovations to leading multiple startups and eventually founding Doppler, a centralized cloud secret management tool.
Brian emphasizes the importance of making security tools enticing for developers, comparing it to making vegetables taste like candy, to boost productivity. His team’s strategy revolves around seamless integration into developers’ workflows, featuring a VS Code extension and automatic syncing akin to Dropbox, enhancing efficiency and ease of use.
They explore Doppler's competitive edge and how it partners with major cloud resource managers, making two-click integrations effortless. Brian also discusses their customer-centric development approach and the release of enterprise features like two-person approval and config inheritance, designed for complex organizational needs.
Brian's entrepreneurial journey is marked by significant pivots driven by frustration and market demand, rather than strategic planning alone. He shares candid thoughts on the impact of founders and success, cautioning against the allure of celebrity status and emphasizing team contributions.
Join them as they dive into insightful discussions on building developer-friendly security tools, the nuances of secret management, and Brian's perspectives on startup growth and innovation. Discover how Doppler is revolutionizing secret management, one integration at a time.

Socials* LinkedIn: Brian Vallelunga

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In today's episode, Michael and Ben discuss peer review, specifically Michael's experiences. Michael explains his unconventional path, starting with advanced math as a child, then struggling with a math-heavy computer science program in college. He pivoted to environmental studies, focusing on side projects and extracurriculars. These projects led to his first job, and later to a role at a boxing streaming service (2B) with a rigorous peer review process. Ben asks about the importance of the peer review process, and Michael highlights its value in catching errors and ensuring code quality, especially when working under pressure.

Moreover, Ben discusses the learning experience at different career stages, noting that junior developers learn from senior developers' code and feedback. Ben discusses the differences in peer review for different types of code changes. They discuss the importance of thorough review for critical code changes and many more!

Socials
* LinkedIn: Ben Wilson * LinkedIn: Michael Berk

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In today's episode, Ben and Michael discuss how to handle situations involving individuals lacking expertise in machine learning projects. They explore scenarios where a team lacks expertise, considering approaches for consultants or team members. They discuss various personality types encountered in such situations, including those overly suspicious or resistant to change. Moreover, they discuss how to convince a boss that a proposed project is a bad idea, suggesting a structured approach with clear estimates, risk assessment, and alternative solutions. They emphasize the importance of honesty, transparency, and presenting options with clear pros and cons.
The discussion then returns to the Gen AI time-series case study, suggesting a presentation of multiple options, including established algorithms and the Gen AI approach, to facilitate a data-driven decision.
Finally, the episode addresses the scenario of a teammate being untrained about a system they built, suggesting a combination of direct but constructive feedback and a collaborative approach to identify the root cause of the issue.

Socials
* LinkedIn Ben Wilson * LinkedIn Michael Berk

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In this episode, Michael and Ben dive deep into the intersection of education and technology with their insightful guest, Daniel Hiterer.
Michael, a data engineering and machine learning expert, and Ben, an integrator of Gen AI tools, navigate through Danny's unique perspective on the impact of nurturing educational environments. Currently working at Cornell’s Studio entrepreneurship program, Danny brings a multidisciplinary background, combining history and instructional technology, and shares his vision for the future of learning.
This episode explores the transformative power of nurture in education, the evolving role of Gen AI in fostering curiosity, and the challenges and opportunities in integrating AI into the learning process. Danny provides thought-provoking insights on emotional access points, curiosity-driven learning, and the delicate balance between educational goals and productivity tools.
Listen in as they discuss personalized education, the promise of AI-assisted learning, and the future trajectory of superintelligence in education. Plus, hear personal anecdotes from Ben and Michael about their own learning journeys and the evolving landscape of curiosity and knowledge.

Socials
* LinkedIn: Daniel Hiterer

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Today, they dive deep into the fascinating intersection of open-source development and machine learning. Michael and Ben are joined by distinguished guest, Görkem Erkan, CTO and seasoned engineer at Jozu.
Görkem shares his illustrious career journey from Nokia to Red Hat, his contributions to the Eclipse Foundation, and his current focus on MLOps. They explore his passion for open-source projects, the cultural and communication impacts on software design, and the unique challenges posed by integrating open-source frameworks with proprietary systems. Ben provides critical insights on the complexities of managing scalable backend services and the hurdles in translating SaaS offerings to open-source platforms.
Tune in to learn about the innovative practices at Jozu, the role of open communication in team success, and the nuanced debate on maintaining separate proprietary and open-source codebases. This episode is packed with valuable lessons for developers, tech leaders, and anyone interested in the future of machine learning and open-source development.

Socials
* LinkedIn: Görkem Ercan

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In this week's episode, Michael and Ben sit down with Artem Koren, Chief Product Officer at Sembly AI, to explore the future of AI integration in the workplace. We'll delve into Sembly AI's mission to accelerate team efficiency through powerful AI tools—imagine an Iron Man suit for your daily tasks. From proactive AI assisting with time-consuming tasks to ethical considerations in data privacy, this episode covers the cutting-edge developments and challenges in AI implementation.
They also discuss the evolving landscape of workplace automation, the intricacies of data collection, and the balance between privacy and productivity. They also highlight Sembly's latest advancements like Semblian 2.0, a breakthrough in digital twin technology that promises to redefine meeting productivity. Join them for an in-depth conversation on AI's transformative potential, the ethical responsibilities it entails, and the practical impacts on the project.

Links
* Semblian 2.0

Socials
* LinkedIn: Artem Koren

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In today's episode, Michael is joined by Hikari Senju the Founder and CEO at Omneky. He starts by discussing how he built Omneky, an AI-Driven Marketing Platform. They dive into Hikari's approach to working with customers on brand strategy and content. They also talk about the increasing importance of brands in a digital, AI-driven world. Additionally, they tackle Hikari's perspective on how generative AI will impact the advertising industry. Tune in on how ML is Reshaping The Advertising Industry.

Socials
* LinkedIn: Hikari Senju

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Today, Ben and Michael dive into a compelling discussion on the intricate dance between challenges, feedback, mentorship, and growth in the field of software development. In this episode, Michael shares their journey of overcoming the pains of independent problem-solving before receiving effective guidance. As we explore their experiences with Ben, they uncover the vital importance of openness to feedback and the profound value of peer review in refining solutions.

They delve into technical aspects, including Python's Pytest framework for unit tests and the delicate balance between complexity and simplicity in testing for maintainability and readability. Additionally, they touch on Michael's hands-on learning curve, tackling unfamiliar concepts such as RAG, embeddings, LLMs, and Git development, all while managing significant time constraints and social commitments.

Moreover, Ben shares his mentorship philosophy, likening it to military training—pushing mentees to their limits without prior warning to foster resilience and self-improvement. They also discuss the importance of documentation, bug bashes, and the fine art of balancing integration and unit tests to ensure robust and thorough software.

Join them as they explore the journey from initial struggle to increased autonomy and confidence, using real-world examples of testing gaps, code complexities, and the powerful impact of daily feedback. Whether you're a seasoned developer or just starting your tech career, this episode is packed with valuable insights to enhance your learning and development process. So, stay tuned and dive right in!

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Michael Berk dives deep into the adventures of AI and machine learning with our special guest, Richmond Alake, a staff developer advocate at MongoDB. Richmond's journey from web development to AI was driven by a quest for excitement and new challenges. In this episode, he shares how he transitioned into the AI field, his passion for using writing as a learning tool, and the importance of continuous learning in evolving tech landscapes.

They explore the intricacies of building and evaluating Retrieval-Augmented Generation (RAG) systems, the benefits of MongoDB's versatile database functionalities, and the pressing challenges in machine learning data collection and evaluation. Richmond also gives us a peek into MongoDB's advanced solutions for AI application development and how strategic data chunking can impact efficiency.
Whether you're a budding AI enthusiast or an experienced developer looking to expand your horizons, this episode is packed with practical advice, career insights, and the latest trends in AI and machine learning. Stay tuned as we uncover how to navigate the complexity of RAG pipelines and the evolving landscape of generative AI. Let's get started!

Socials
* LinkedIn: Richmond Alake

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Today, we have a special guest Abi Aryan, an accomplished founder of Abide AI and a seasoned expert in machine learning. Joining us are your hosts, Michael Berk and Ben Wilson, who bring a wealth of experience from Databricks.
In this episode, Ben shares his journey navigating the intricacies of deep learning and the surprising effectiveness of simpler solutions over complex algorithms. Abi lends her insights to the balancing act between innovation and practicality in tech adoption, influenced by career stability and venture capital demands. They also explore Abi's passion for recommender systems and audio speech synthesis, and the potential these fields hold for e-commerce and inclusivity.
Abi also gives us a glimpse into her research methodology, her approach to autonomous agents, and the challenges she faced with bias and imposter syndrome. As they dissect consulting strategies, experiment design, and the art of fostering a collaborative environment, this episode is packed with valuable lessons for any tech enthusiast.
So, get ready to tune in, take notes, and be inspired by the fascinating stories and insights from our expert guest and hosts.

Socials
* Abi Aryan * LinkedIn: Abi Aryan

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Today, host Michael Berk and Ben Wilson dive deep into the multifaceted world of software engineering and data science with their insightful guest, Sandy Ryza a lead engineer from Dagster Labs. In this episode, they explore a range of intriguing topics, from the impact of the broken windows theory on code quality to the delicate balance of maintaining backward compatibility in evolving software projects.
Sandy talks about the challenges and learnings in transitioning from data science back to software engineering, including dependency management and designing for diverse use cases. They touch on the importance of clear naming conventions, tooling, and infrastructure enforcement to maintain high code quality. Plus, they discuss the intricate process of selecting and managing Python libraries, the satisfaction of refactoring old code, and the necessity of balancing new feature development with stability.
Michael and Ben will guide us through these essential discussions, emphasizing the significance of user-centric API design and the benefits of open source software. They also get practical advice on navigating API changes and managing dependencies effectively, with real-world examples from Dagster, Spark Time Series, and the libraries Numba and Pydantic.
Join them for an episode packed with valuable insights and strategies for becoming a top-end developer! Don’t forget to follow Sandy on Twitter and check out Dagster.io for more information on his work.

Socials
* LinkedIn: Sandy Ryza

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In today's episode, Ben and Michael dive deep into the intricacies of software development, innovation, and team dynamics. This episode explores the critical balance between building in-house tools versus leveraging open-source solutions, with real-world examples from Databricks.
They discuss the creation and eventual abandonment of a benchmarking tool for warehouses and discuss the importance of evaluating user demand, effort, and impact before committing to development. They emphasize the role of empathy, constructive feedback, and team collaboration in driving successful projects. They share strategies to influence behavior within organizations, the significance of a blame-free culture, and the art of leading difficult conversations with stakeholders.
From detailed discussions on customer feedback loops to practical advice on automating mundane tasks, this episode is packed with insights that will help you navigate the complex landscape of software development. So sit back, relax, and join us for a thoughtful and engaging conversation on how to turn challenges into opportunities for growth and innovation.

Socials* LinkedIn: Michael berk * LinkedIn: Benjamin Wilson

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In today's episode, Ben and Michael dive deep into the intersection of education, AI, and innovative instructional design. Luis Garcia who is the President of PETE, delves into automating instructional design, content development, and assessments, shedding light on the evolving educational landscape and the pivotal role of evaluation and learning. Ben shares invaluable insights on leveraging chat GPT and generative AI to streamline documentation creation and evaluate knowledge, drastically cutting down processing times.
Together, Luis and Ben discuss the positive reception and transformative potential of AI-driven micro-courses, text-to-speech features, and customized training tools in education. They also touch on the intense training involved in fields like nuclear reactor operation and the need for effective onboarding processes. Michael contributes by emphasizing empathy and strategic pacing in international business projects, while also summarizing instructional strategies and organizational tips for rapid learning and growth.
Join them as they explore the crucial role of innovative AI technologies and personalized learning tools in reshaping education and business training, featuring insights from top industry professionals and thought leaders. And don't miss the chance to learn more about Pete and Collectiva. Get ready for a compelling discussion about enhancing learning outcomes and the future of education with AI!

Socials
* LinkedIn: Luis E. Garcia

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In today's episode, our hosts Michael, Ben, and special guest Keith Goode delve deep into the transformative role of AI and machine learning in modern HR practices. They tackle a range of topics, starting with the innovative use of AI to streamline surveying and sentiment analysis in employee evaluations. They explore the exciting potential of AI models in technical data collection, particularly for interviews, and discuss how these models can assess candidates' sentiment and confidence levels, providing valuable insights into their fit for specific roles.
They also hear about the emerging trends discussed at the recent Databricks Data and AI Summit, where generative AI for resume screening took center stage. They debate the challenges and opportunities of leveraging AI to reduce information overload in analytics, particularly within the complex hiring process. They emphasize the importance of explainable AI models, consulting scalability, and the perennial issue of data cleansing in HR.
Additionally, the episode touches on the critical aspects of diversity and inclusion in the workplace, the influence of new legislation on workforce diversity modeling, and how companies can configure HR systems to suit their unique needs. They share insights into using advanced tools like XGBoost for predictive modeling, highlight the significance of face-to-face interactions in interview processes, and caution against over-reliance on automated resume screening.
Join them as they navigate these thought-provoking discussions and more, shedding light on the intersection of AI, machine learning, and human resources.

Socials
* LinkedIn: Keith Goode

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In today's episode, they delve deep into the intertwining worlds of technology, security, and innovation with Aaron Painter, CEO at Nametag.
Aaron kicks things off by underlining the cultural facets in hiring, emphasizing the virtues of being good listeners, intellectually curious, kind, and respectful while achieving tangible results. We also explore the collaborative spirit in group product planning and the pivotal role of diverse perspectives.
From there, Ben takes us into the fascinating—and somewhat unnerving—advancements in deep fakes, particularly in image generation, and their implications for security and entertainment. This discussion also touches on the complexities of preventing deep fake attacks and the critical role of technology in mitigating these threats.
Michael weighs in on how physical devices and user verification limit fraudulent deep fake activities, while Aaron offers invaluable advice on latching onto growing fields like AI for future-proofing your career. We also delve into a riveting recount of Ben’s early data science days, offering a glimpse into the tech evolution from Hadoop to cloud computing.

Our conversation spans intriguing analogies, from the oil industry to AI, and examines the crucial shift toward cloud technologies, underpinned by end-use cases and consumer demands. We discuss the pressing need for secure identity verification in the digital age, exploring multifactor authentication and the delicate balance between security and user experience. Additionally, the episode covers Microsoft’s impact on global economies, with Aaron sharing heartfelt insights from his illustrious career.
Join them as they navigate these compelling topics and more, offering a wealth of knowledge for developers, tech enthusiasts, and anyone keen on the future of technology. Tune in and prepare to elevate your understanding as we unfold the latest in machine learning, AI, and technological innovation.

Socials
* LinkedIn: Aaron Painter

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In today’s episode, they dive into the intricate world of MLOps with Brad Micklea, a seasoned expert with extensive experience in software infrastructure and leadership roles at Eclipse Shay, Red Hat, AWS, and Jozu. Brad shares his journey of founding Jozu, an MLOps company that stands out with its commitment to open standards such as the OCI standard for packaging AI projects. Alongside Jozu, they explore KitOps, an innovative open-source project that simplifies version control and collaboration for AI teams.

Join them as they discuss the challenges in integrating AI models into production, the importance of monitoring API usage, and the critical role of automated rollback systems in maintaining operational excellence. They also touch on the cultural differences in operational approaches between giants like AWS and Red Hat and hear first-hand experiences on the significance of transparency, trust, and efficient risk management in both startups and established companies.

Whether you're a DevOps professional, MLOps practitioner, or data scientist transitioning to production, this episode is packed with valuable insights and practical advice to help you navigate the complexities of AI project management. Tune in to discover how Brad and his team are tackling these challenges head-on and learn how to set up your projects for success from the ground up!

Socials
* LinkedIn: Brad Micklea

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In today's episode, they dive deep into the evolving landscape of software development. Join us as Kirk, the CTO and founder at Graphlit, shares his journey from traditional software at Microsoft to pioneering perception ML for drone-based aerial intelligence. They explore the paradigm shift from object-oriented to functional programming, the crucial role of software architecture, and the challenges of maintaining consistent design and documentation in growing teams.
They also get insights into Databricks' approach to user-friendly API design and the importance of learning management systems in knowledge distillation. Listen in as our speakers discuss the strategic decisions in scaling products, the nuances of open-source contributions, and the value of automation in modern development. Whether you're navigating a startup or a large enterprise, this episode is packed with expert advice on building robust, scalable systems and the dynamic decision-making needed to thrive in today's tech environment. Tune in and elevate your development game!

Socials
* LinkedIn: Kirk Marple

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In today's episode, Michael and Ben alongside our guest Alex Levin dive deep into the evolving landscape of AI development and its broader implications on business and society. You'll hear Ben emphasize reducing the cost and time of AI development by leveraging open-source models, while Alex draws parallels between the AI industry and flat-screen TVs, advocating for AI as a public good.
The conversation traverses through the importance of compelling AI services, revenue-generating strategies, and the disruption AI brings—both in job creation and efficiency improvement. From personal anecdotes in semiconductor fabs to the pitfalls of the YC funding model, we explore various facets of success in the tech world. Alex brings a unique perspective from his background in psychology and entrepreneurship, touching on the importance of market timing, embracing uncertainty, and the significant role of mentorship.
Whether you're a startup enthusiast or a seasoned tech veteran, this episode will provide invaluable insights on navigating the complexities of AI development, operational challenges for founders, and the essential balance between innovation and business strategy. So tune in, and let's get started on this journey through the cutting edge of technology with our insightful guests on Top End Devs!

Socials
* LinkedIn: Alex Levin * alexrlevin.com

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Projjal Ghatak is the Founder/CEO at OnLoop. They dive deep into what it means to achieve true greatness in the software development sphere. Is it just about technical prowess, or does it involve something more substantial?

In today's episode, Michael and Ben dissect the process of building maintainable and impactful products, emphasizing the crucial balance between innovation and simplicity. They explore personal and group learning curves, the value of collaboration, and the indispensable role of peer review in creating robust solutions.

They'll also touch upon the nuanced perspectives of working at top tech companies like Google and Databricks, examining how timing and project involvement can shape a developer's skillset and career trajectory. From the importance of understanding one's career goals to the powerful impact of a company's culture on code quality, they aim to uncover the multifaceted aspects of professional growth in tech.

Join they as they delve into stories of overengineered solutions, the necessity of constructive feedback, and the collaborative efforts that define truly great products. Whether you're aspiring to join the elite 1% of developers, or simply looking to understand the dynamics of a high-functioning team, this episode is packed with insights and practical advice. So, tune in and let's explore the path to greatness together!

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In today's episode, Michael Berk and Ben Wilson dive deep into the intricacies of technical interviews for machine learning roles. They discuss the importance of assessing candidates' genuine knowledge of traditional and deep learning models and the value of being candid about one's expertise.
They explore how technical skills, particularly in applied machine learning, are evaluated with a focus on their impact on business outcomes. Michael and Ben also address the common misalignments between job descriptions and the actual skills required, stressing the need for problem-solving capabilities and critical thinking over memorized knowledge.
Additionally, they delve into the roles within data science—analysts, applied ML specialists, and researchers—highlighting the importance of fitting the right skills to the right job. They also touch on the evolving expectations and frustrations with the current hiring process, offering insights on how it can be improved.
Stay tuned as they unpack these topics and more, including valuable tips for showcasing your skills effectively on resumes, and the significance of asking insightful questions during interviews. Whether you’re an aspiring data scientist or a seasoned professional, this episode is packed with practical advice and industry insights you won’t want to miss!

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Michael Berk and Ben Wilson from Databricks are joined by Brooke Wenig, who has a fascinating background in distributed machine learning. Today’s conversation dives deep into the intersection of AI, environmental science, and career transitions. They explore how individuals like Michael transformed their careers from environmental science to AI, leveraging existing expertise in innovative ways. Ben shares insights on leaping from non-technical roles to data science by embracing automation with Python and machine learning.
We tackle the critical shift in roles, the balance between education and hands-on experience, and the growing disparity between academia and industry. Brooke brings valuable perspectives on project scoping, from aligning success criteria to ensuring real-world value. The discussion revolves around augmenting existing roles with AI, common pitfalls, and transitioning proofs of concept to production.
They also explore the practical applications of language models, the debate over open versus closed source models, and the future of AI in various industries. With a focus on collaboration, the traits of top data scientists, and the implications of integrating AI into non-tech fields, this episode is packed with insights and tips for anyone looking to navigate the exciting world of AI and machine learning.
Join them as they delve into these topics and more, discussing the evolving landscape of AI and how it's shaping careers and industries alike.

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Michael Berk and Ben Wilson join cybersecurity expert Daniel Miessler to delve into the cutting-edge world of AI and cybersecurity. They discuss the evolving tactics of attackers, from specialized targeting to AI-driven data collection. The episode tackles dynamic risk assessment, the arms race between attackers and defenders, and the role of open-source models in security.
They explore AI's potential to monitor, defend, and even augment human efforts against security threats, touching on both the opportunities and ethical challenges. They also examine AI's role in protecting against social media scams and phishing attacks, envisioning a future where AI acts as our digital guardian.
Whether you're in cybersecurity, development, or simply curious about AI's impact on security, this episode is packed with valuable insights. Stay tuned for a fascinating discussion!

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Fernando Lopez is an AI Engineer at Google. They delve deep into the realms of machine learning, documentation challenges in open-source projects, and the transition from startup environments to tech giants like Google. They share their candid experiences with impostor syndrome, practical tips for continuous learning, and the nuances of scaling solutions in the dynamic tech landscape.
Explore the nuances of software development, the complex interplay of learning strategies, and the realities of navigating large-scale organizations. Join them as the industry experts unravel the intricacies of prototyping, scaling challenges, and the value of hands-on experience in shaping successful tech careers. Get ready to immerse yourself in a wealth of knowledge and thought-provoking insights that underscore the essence of growth and innovation in the tech realm.

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Deeksha Goyal is the Senior Machine Learning Engineer at Lyft. They delve into the intricacies of machine learning and data-driven technology. In this episode, they explore the challenges and innovations in deploying models into production, particularly focusing on the real-world implications of ETA (Estimated Time of Arrival) modeling at Lyft. They share valuable insights, from the complexities of A/B testing and long-term impact assessment, to the dynamic nature of handling real-time data and addressing unpredictability in route predictions. Join them as they journey through the world of model deployment, bug identification, and career development within the fast-paced environment of Lyft's data-driven infrastructure.

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Matt Van Itallie is the Founder & CEO at Sema. This episode covers a wide range of topics, from the impact of AI and machine learning on software development and educational systems, to the importance of code reviews and career advice in the tech industry. Matt Van Italy shares his diverse experiences in law, consulting, public schools, and the tech sector, emphasizing the value of using data to drive improvements.
The conversation also touches on the use of GenAI tools in development and the need for organizations to embrace new technology to stay competitive. They also explore issues such as defense spending, career transitions, and the significance of investing in education and human capital.
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Lukas Geiger is a Deep Learning Scientist, open-source developer, and an astroparticle physicist. He shares his experience using machine learning to analyze cosmic ray particles and detect secondary particles. We explore the challenges and opportunities of open source as a business model, the potential of models for edge computing, and the importance of understanding open-source code. Join us as we delve into the intersection of physics, machine learning, and the intricate world of software development.
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Nick Schrock is the Founder of Dagster Labs. He is also the Creator of Dagster and the Co-creator of GraphQL. They delve into the world of data engineering, software development, and ML orchestration. In today's episode, they explore the challenges and intricacies of standardizing data movement, handling data access in various systems, and migrating data across different platforms. They share insights on the importance of building a system that spans multiple data platforms, the decision-making process behind tool development, and the impact of lineage in managing and migrating data. Join them as they uncover the complexities of open-source projects, API evolution, and the future of data engineering.

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Ben and Michael dive into the dynamic relationship between engineers and scientists in the realms of software engineering and physical science. They explore the differences and similarities between these roles, sharing valuable insights on the research and testing processes, the importance of thorough research, the value of teamwork, and the challenges of transitioning between engineering and science. With analogies, real-world examples, and expert perspectives, they shed light on the intricacies of these roles and the considerations for hiring scientists and engineers based on company size and market effects. Tune in for a thought-provoking discussion on finding the optimal path between efficiency and innovation in the world of technology and research!

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Michael and Ben dive into the critical role of design in software development processes. They emphasize the value of clear and understandable code, the importance of thorough design for complex projects, and the need for comprehensive documentation and peer reviews. The conversation also delves into the challenges of handling complex code, the significance of prototype research, and the distinction between design decisions and implementation details. Through real-world examples, they illustrate the impact of rushed processes on project outcomes and the responsibility of tech leads in analyzing and deleting unused code. Join them as they explore how process and organizational culture contribute to successful outcomes in tech companies and why companies invest in skilled individuals who can work efficiently within established processes.

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Michael and Ben share their insights on being called in to fix issues in production systems at the last minute. They stress the importance of asking questions to understand the context and navigate the political landscape, and caution against providing half-baked solutions. They also discuss the significance of understanding project goals, documenting decision-making processes, and providing guidance to the team to avoid building unnecessary and difficult-to-maintain systems. Stay tuned as they share their experiences and valuable advice for navigating complex projects and delivering meaningful solutions.

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Ben and Michael dive into the world of machine learning operations (MLOps) and discuss the complexities of building a computer vision pipeline to detect fishing boats at ports. They unpack the intricacies of MLOps basics and the challenges of implementing an effective computer vision model for traffic optimization and data collection at ports. From discussing the importance of exploratory data analysis (EDA) and data cleaning for image classification to the intricacies of continuous integration and deployment, this episode provides invaluable insights into the practical application of machine learning in real-world scenarios.Sponsors* Chuck's Resume Template * Developer Book Club * Become a Top 1% Dev with a Top End Devs Membership

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Ben and Michael dive into the complex world of decision-making, transparency, and truth-seeking in professional settings. They share their insights on challenging decisions, navigating organizational hierarchies, and the importance of evidence-based arguments. From the intricacies of software development to the dynamics of leadership, they discuss the challenges and strategies for making informed decisions and seeking truth within organizations. Whether you're a tech lead, director, or aspiring leader, this episode offers valuable perspectives on humility, empathy, and effective communication in the fast-paced world of technology.
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Davis King is the perception engineer at Aurora. They talk about Dlib, which makes real-world machine learning and data analysis applications. They delve into the complexities of CUDA extensions, software layering, and the critical role of accurate data in machine learning. Join them as they dissect the challenges and importance of creating well-structured software with clear APIs, the intricacies of real-time systems, and the impact of language choice on code complexity and maintenance.
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Links * Dlib.net

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Ben and Michael delve into the crucial aspects of coding, culture, and collaboration. From the importance of proper formatting and consistency in Python code to the challenges of changing organizational culture, they explore the impact of code quality on team dynamics and project success. They emphasize empathy, communication, and the power of a positive vision to drive change. Tune in to gain insights on tackling diverse problems, the role of documentation, and the significance of modularization in codebases. Join them as they navigate the world of development and seek to create a positive work environment where clear, understandable code thrives.
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Konstantin Gizdarski and Jonas Timmermann are software engineers at Lyft. They dive deep into the world of machine learning and engineering at Lyft. Join them as they explore the challenges and successes of implementing reinforcement learning, contextual bandits, and advanced AI technologies in a real-world business environment. Learn about the collaborative engineering culture at Lyft, the development of new ML capabilities, and the unique approaches to infrastructure and model deployment. Listen in as industry experts share their insights on accelerating decision-making processes, simplifying tools for end users, and finding innovative solutions to common engineering challenges.
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James Lamb is a senior software engineer at NVIDIA. They delve into the world of open-source contributions and the impact of traditional machine learning on the modern economy. James shares his journey of becoming a maintainer of renowned open-source projects while offering valuable insights into the benefits and motivations behind contributing to the community.Join them as they explore the significance of human review in the PR process, the value of automated feedback, and the importance of maintaining a positive and inclusive environment for contributors in open-source projects.
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Richard Berk delves into the exciting world of machine learning in a thought-provoking discussion on a wide range of topics. They explore the potential for Westworld-style androids, considerations in the criminal justice system, ethical implications of AI in warfare, and the challenges of understanding uncertainty in real-world data. From advanced language models and genetic algorithms to the impact of AI on everyday life, get ready for a fascinating and insightful conversation that will expand your understanding of the evolving landscape of machine learning.

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Michael and Ben take a deep dive into the insightful journey spanning nuclear engineering, software development, and mentorship at Databricks. They delve into the complexities of career progression, the importance of humility and honesty in mentoring relationships, and the value of fostering a collaborative engineering culture. Join us as we explore the pivotal moments and impactful lessons that have shaped Ben's career and influenced their perspective on the ever-evolving tech industry.
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Piotr Skalski is a computer vision engineer at Roboflow. They dive deep into the world of computer vision and AI technology, exploring the utilization of video, photo, and imagery data in array processing and NLP. They discuss the challenges and opportunities presented by these fields, as well as the impact of AI technology on various industries, including civil engineering and manufacturing. Join us as we explore the fascinating intersection of technology, innovation, and the future of software development.
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Get the Black Friday/Cyber Monday "Double Your Productivity by 5pm Today" DealCoupon Code: "THRIVE" for a GIANT discount
Are you looking at all the layoffs and uncertainty going on and wondering if your company is the next to cut back?
Or, maybe you're a freelancer or entrepreneur who is trying to figure out how to deliver more value to gain or retain customers?
Mani Vaya joins Charles Max Wood to discuss the one thing that both of them use to more than double their productivity on a daily basis.
Mani has read 1,000's of productivity books over the last several years and has formulated a methodology for getting more done, but found that he lacked the discipline to follow through on his plans.
The he found the one thing that kept him on track and made him so productive that he is now getting all of his work done and was able to live the life he wants.
Chuck also weighs in on how Mani's technique has worked for him and allows him to spend more time with his wife and kids, run a podcast network, and a nearly full time contract.
Join the episode to learn how Chuck and Mani get into a regular flow state with their work and consistently deliver at work.

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In today's episode, we speak with Neil Theise, a pathologist at NYU and author of Notes on Complexity: A Scientific Theory of Connection, Consciousness and Being. Expect to learn about complexity theory and its implications for sentience, how great ideas are formed, whether AGI can be built with silicon-based computers, and much more!
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In today's episode, we speak with Agata Checinska (Spotify) and Kasia Batko-Toluc (Citizen Network Watchdog Poland) about data privacy, accessibility, and accuracy. Expect to learn about how Poland approaches these sensitive topics, the power of deep fakes, and much more!
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Socials* LinkedIn: Agata Checinska * LinkedIn: Katarzyna Batko-Toluc

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In today's episode, we speak with Pierre Eliseeff, co-founder of Analyzr and causal inference expert. Expect to learn a 3-step blueprint for doing causal analysis, thinking critically about data, creating successful projects, and much more!
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In today's episode, we chat with Sylvain Lesage from Hugging Face, a specialist in data visualization and software engineering. Dive in to discover insights about Hugging Face's software engineering environment, invaluable data visualization techniques, and more!
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In today's episode, we chat with Adam Ross Nelson - a data scientist, career mentor, and writer. Our primary focus is understanding when you've achieved "good enough" skills. Additionally, we explore team dynamics, the importance of soft skills, and offer career guidance for budding data scientists.
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Links* Data Science Career Accelerator * How to Become a Data Scientist * Confident Data Science

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In today's episode, we delve into a popular topic at Databricks: building recommendation engines. We'll guide you on how to plan such projects, measure your model's success, suggest effective algorithms, and cover many other insights!
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Ben and Michael delve into the world of data science and software development. They discuss the importance of setting standards and documentation in project development, the struggle of reviewing and maintaining code changes, and the need for programmatic solutions to automate review processes. They explore the journey of building prototypes, tackling uncertainties, and the quest for a more reliable and efficient review process.
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In today's episode we speak with Paul Allen, the creator of Ancestry.com. Currently, he is CEO of Soar.com, a platform that leverages AI to make you live a more fulfilled and effective life. Expect to learn about applying LLMs to personal productivity, how Paul thinks about viral content, new applications for LLMs, and much more!
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In today's episode, we speak with Anand Das, the CTO and co-founder of bito.ai, an LLM-powered code assistant. Expect to learn about managing LLM context, keeping LLMs up-to-date, common user pitfalls, and much more!

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In today's episode, we speak with Shivek Sachdev, product owner at a seed-to-sale Cannabis company. Expect to learn about disrupting new industries, applying LLMs to daily work, and much more!

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In today's episode we speak to Eric Daimler, a White House Presidential Innovation Fellow, professor at Carnegie Mellon, and the current CEO of Conexus. Expect to learn about how AI is impacting the military, general workforce, and our understanding of sentience! Oh, and make sure you use farm animals when explaining technical concepts.

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In today's episode, we speak with Jeff Procise, founder of Wintellect, an Azure-focused software consulting company. Expect to learn about your next LLM MVP on Azure, the societal impact of AI, the bifurcation of model size, and much more!

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Have you ever written code and thought, "hmm, I wonder if my teammates would use this." Well in today's episode, we show you how to go from concept to production-level code. Spoiler alert: you're going to have to write tests!

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In today's episode, Michael and Ben break down some surefire methods to be successful. If you follow these tips, you are guaranteed to co-found the next Google. Some topics include time boxing exciting work, tips for growing documentation, pitching to diverse crowds, and much more!

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In today's episode, we speak with Netflix ML engineer Amir Ziai. Expect to learn about building ML tools for stakeholders, the pros and cons of a Netflix-like culture, and Amir's strategy for learning.

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In today's episode, we dive into Ben's experience in navigating the career ladder. Expect to learn why your leveling matrix is probably wrong and how you should actually spend your time to maximize career growth.

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In today's episode, we speak with Roman Grebennikov, an expert in ranking algorithms. Expect to learn about his open source project, the difference between retrieval and ranking, and much more!

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Socials* Roman Grebennikov

In today's episode, we walk through Ben's experience creating the Hugging Face transformer flavor for ML flow. During this case study we highlight the structure he uses to learn new technologies and cover some practical tips along the way.

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Today we speak with ex-Googler, Praveen Paritosh. He has over 20 years of experience as a research scientist and has worked on some of AI's most impactful projects. Expect to learn about scientific innovation, the importance of data, and the next wave of AI (spoiler, it's not LLMs).

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Host from the Ruby Rogues podcast, Dave Kimura joins Ben and Michael for this week's crossover episode. They discuss applying machine learning, deep learning, and algorithm. They also dive into how artificial intelligence changes the future.

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Today we speak with Noah Silbert, a former data scientist at Netflix and current data scientist at Tubi. Expect to learn about company size can impact your role and day-to-day work as a data scientist. We also cover Noah's experience moving from being a professor to handling the scale and complexity of the tech industry.

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Host from the Ruby Rogues podcast, Dave Kimura joins Ben and Michael for this week's crossover episode. They discuss applying machine learning, deep learning, and algorithm. They also dive into how artificial intelligence changes the future.

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Are you dissatisfied with your job? Sam Feeney helps organizations improve employee engagement, increase retention, and reinvent hiring while helping individuals (re)discover career satisfaction in their current roles. He joins the show alongside Chuck Wood to tackle altering the way you perceive your job and talk about Career satisfaction.

On YouTube

How Do You Stop Hating Your Job? - BONUS

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LinkedIn: Sam Feeney

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Today we speak with a software engineer who is interested in becoming an ML engineer. Expect to learn about ML roles that are most attainable based on a strong software engineering skill set. We also cover some tangible strategies you can leverage to make the transition.

On YouTube

How to Transition from Software Engineer to ML Engineer - ML 111

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Today we look at an applied use case for ML: developing intelligent meeting notes. Expect to learn about LLMs, AI assistants, and how to develop an AI startup.

On YouTube

Machine Learning for Meeting Notes - ML 110

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Socials

LinkedIn: Artem Koren

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Today we deep dive into the mind of two brilliant Databricks software engineers. Their primary project was building the model serving feature, but expect to learn about ML side projects, traits of successful software engineers, and much more!

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Hosts of the Adventures in DevOps podcast, Jillian Rowe and Jonathan Hall, join Ben and Michael on this week's episode crossover. They talk about the intersection of ML and DevOps. They dive into the concepts and differences between ML and DevOps. Additionally, they talk about how ML ideas may be applied to DevOps principles and vice versa.

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ChatGPT is the most robust free chatbot. It can answer questions, write code, and summarize text. Today we will talk about the creation of ChatGPT, its implications for society, and how the model actually works.

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Today we look at an applied use case for ML: parsing movie scripts. Expect to learn about bringing ML to new industries, the future of Large Language Models (LLM), and automation in the movie industry.

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LinkedIn: Ruslan Khamidullin

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"Any sufficiently advanced technology is indistinguishable from magic."

Today, Michael and Ben talk about the broad implications of ChatGPT and similar algorithms. Expect to learn about...

The difference between AI and ML
General Artificial Intelligence
Some personal opinions about the overlap between "the divine" and AI

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Today we speak with a staff data scientist at Walmart who specializes in forecasting. He has built an open-source tool that allows you to leverage tabular data in PyTorch. He also has written a book on time series forecasting with deep learning.

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How do you develop ML code? Do you use notebooks or do you use IDEs? In this episode, we get some practical advice from both Ben and our guest on leveraging software principles to write better code in both an IDE and notebook environment. We'll also learn about a cool new Databricks feature that will help you run ML code from an IDE.

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In this week's episode, we meet with Micheal McCourt, the head of engineering at SigOpt. He is an industry expert on optimization algorithms, so expect to learn about constraint-active search, SigOpt's new open-source optimizer, and how to run an engineering team.

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Have you ever wondered how to secure a cloud deployment? Well, today we talk to the president at a cloud security company about personal security, detecting malicious actors, startup trends, and much more!

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Have you ever wondered about the most promising industries in Machine Learning? Today we will learn from Avi Goldfarb, the chair of AI at the University of Toronto, about...

The most promising AI industries
Potential problems with powerful AI
The economics behind innovation

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In this episode, Ben talks with Rosaria Silipo, a Software Engineer and Developer Relations advocate at Knime. They discuss the benefits of low-code ML, delve into the history of ML development work as it has changed over the past few decades, and discuss a few stories about the importance of pursuing simplicity in implementations.

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Corey Zumar talks about the new release of MLflow, 2.0, and what the new major features that are included in the release. Bilal and Corey then discuss managing feature implementation priorities, and selling large-scale project ideas to internal customers, end-users, executives, and the dev team. The discussion also centers around generalizing feature requests to implementations that will work for the masses and how to effectively do prototype releases for incremental agile development for complex projects.

Sponsors* Chuck's Resume Template * Developer Book Club starting with Clean Architecture by Robert C. Martin * Become a Top 1% Dev with a Top End Devs Membership

Links* GitHub: Corey-Zumar

Do you multitask? If so, you'll want to check out this episode. We'll cover...

The difference between good and bad context switching
Some personal stories about context switching on projects
Practical tips you can leverage to improve your productivity

Sponsors* Chuck's Resume Template * Developer Book Club starting with Clean Architecture by Robert C. Martin * Become a Top 1% Dev with a Top End Devs Membership

Have you ever wondered how to prioritize your ML projects? Today we will talk about...

The difference between applied ML and ML frameworks
How to effectively scope projects via Heilmeier's Catechism

Sponsors* Chuck's Resume Template * Developer Book Club starting with Clean Architecture by Robert C. Martin * Become a Top 1% Dev with a Top End Devs Membership

Sponsors* Chuck's Resume Template * Developer Book Club starting with Clean Architecture by Robert C. Martin * Become a Top 1% Dev with a Top End Devs Membership

Have you ever wondered why data science is hard? Well, in this episode we cover some common data science challenges and how the founders of DagsHub are looking to solve them.

Sponsors* Top End Devs * Coaching | Top End Devs

Links* Dean Pleban

In this show, we cover some practical tips for writing reliable ML code. Here are some of the questions we look to answer...

What are tests and why should you use them?
What's the difference between unit tests and integration tests?
What should you test?
How should you write tests in python? (the answer is to use pytest)

Sponsors* Top End Devs * Coaching | Top End Devs

Charles Simon, BSEE, MSCs is a nationally recognized entrepreneur and software developer who has many years of computer experience in industry including pioneering work in artificial intelligence (AI). Mr. Simon's technical experience includes the creation of two unique AI systems along with software for successful neurological test equipment combining AI development with biomedical nerve signal testing that gives him the singular insight. Today on the show, Charles, Michael, and Ben explore the riveting future of AGI and other illuminating technology concepts. This is an exciting episode you won’t want to miss!

In this episode…* Charles Simon’s extensive background * Classical algorithms vs manual design * Knowledge and power generated in our brains * AI and ML concepts and learning patterns * Graph based approaches and deep learning * “Terminator” and AGI * Future AI and open source simulator * Leveraging technology to solve problems

Sponsors* Top End Devs * Coaching | Top End Devs

Links* The Team * LinkedIn: Charles Simon

Fernando Lopez joins the show today to share his ML insights with a video interview recruiting platform for candidate hiring. Michael and Ben also deep dive into various related ML models and AI topics.

In this episode…
* Core software engineering skills * Practice data algorithms and structures * Working towards production-grade ML * Data engineering and data structures * Using state-of-the-art models * Applying labels to data * The AI revolution * Eliminating bias and unweighted data set * Unconscious and conscious exercises * Complex models with structure

Sponsors* Top End Devs * Coaching | Top End Devs

Links* How to Package and Distribute Machine Learning Models with MLFlow - KDnuggets * Twitter: @ferneutronn

Today the panel discusses high level distributed time series models, using a hot dog stand company as the case study to anchor the understanding with these models.

In this episode…
* Understanding use case * ML flow models and events * KPI forecasts * Metadata outputs * Prediction intervals for hotdog data * Automated time series forecasts * Libraries required for optimization * Practical tips managing the data and * Setting up the data for consumption * Managing black swan events

Sponsors* Top End Devs * Coaching | Top End Devs

Today on the show, the panel discusses time series models, practical tips and tricks, and shares stories and examples of various models and the processes for optimal application in your ML workflows.

In this episode…
* Ben’s time series model for sales forecasting * The flat line model * Examples using time series models * Understanding your data * Lag functions and moving averages * Signal processing in models * Manually adding change points * Deep learning in time series models

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Optical character recognition, or OCR for short, is used to describe algorithms and techniques (both electronic and mechanical) to convert images of text to machine-encoded text. Today on the show, Ahmad Anis shares how he applies Machine Learning to OCR for small hardware applications, for example, blurring a face in a video in real time or on a stream to safeguard privacy using AI. The panel also discusses various strategies related to learning and soft skills needed for success within the industry.

In this episode…
* Optical character recognition (OCR) defined * Multiprocessing vs. multithreading * I/O bound tasks vs. CPU tasks * How to handle a retry in Python * Strategies for employing on small hardware * Template matching and preprocessing * Gray scaling integrations * How to learn and get started within the industry * Reducing the scope and industry soft skills

Sponsors* Top End Devs * Coaching | Top End Devs

Links* LinkedIn: Ahmad Anis * Twitter: @AhmadMustafaAn1

Award winning data evangelist, AI strategist, and innovation leader Vidhi Chugh joins the show today to share her perspective on various topics, including data quality, AI innovation strategies, responsible AI, model intelligence, and much more!

In this episode…
* The importance of innovation * Glorified failure projects * Responsible AI * Data driven companies and quality scores * Tools for autogenerated business insights * Model intelligence * Unspoken assumptions * Understanding the larger picture

Sponsors* Top End Devs * Coaching | Top End Devs

Links* Benefits Of Becoming A Data-First Enterprise - KDnuggets * Top 3 Challenges for Data & Analytics Leaders - KDnuggets * Democratizing Data in Large Enterprises * LinkedIn: Vidhi Chugh

Enjoy this intellectually stimulating conversation with Michael Berk and guest on the show, Aliaksei Mikhailiuk, ML/AI engineer at Snapchat as they discuss everything AI computer graphics to techniques on striking the efficiency-accuracy trade-off for deep neural networks on constrained devices.In this episode…* From academics to machine learning * Machine learning on mobile devices * Cloud computing * Applying AI to computer graphics * Being multi-disciplinary * User experience and latency * Model complexity issues

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Links* Deep Video Inpainting. Removing unwanted objects from videos… | by Aliaksei Mikhailiuk | Towards Data Science * On the edge — deploying deep learning applications on mobile | by Aliaksei Mikhailiuk | Jul, 2022 | Towards Data Science * Seven Questions to Ask before Introducing AI into Your Project | by Aliaksei Mikhailiuk | Towards Data Science * Aliaksei Mikhailiuk * Aliaksei Mikhailiuk – Medium * LinkedIn: Aliaksei Mikhailiuk * Twitter: @mikhailiuka

Adam Ross Nelson helps current and aspiring data professionals enter and level up in the field by uncovering and showcasing their existing data-related talents. Today on the show, Michael interviews Adam to share his various strategies and approaches on how to become a data scientist or make advanced changes in the data science career path.

In this episode…* The “distributed portfolio approach” vs. professional portfolio * Leveraging each platforms strengths * Creating a personal brand * Find others to network and connect with * Create a Rosetta stone in your programming language * Pandas-profiling to contribute to the community * Working with a career coach * Asking the right interview questions

Sponsors* Top End Devs * Coaching | Top End Devs

Links* Data Science Projects Accepting Community Contributions | by Adam Ross Nelson | May, 2022 | Towards Data Science * Adam Ross Nelson * LinkedIn: Adam Ross Nelson * Twitter: @AdamRossNelson

Michael Berk interviews Ken Youens-Clark today to discuss various topics including bioinformatics and programming, plus his career progressions including jazz drumming, technical writing, programming, academia, writing books, and solutions engineering.

In this episode…
* Writing tests and type annotations * Project development lifecycle * Grading programmers with pass / fail * Soft skills within the industry * Bioinformatics and computer science * Prototyping and improving efficiencies

Connect with Ken Youens-Clark via email and LinkedIn

Sponsors* Top End Devs * Coaching | Top End Devs

Links* Mastering Python for Bioinformatics * Command-Line Rust * GitHub - kyclark/command-line-rust * LinkedIn: Ken Youens-Clark * GitHub: kyclark

Data excellence is the foundation of better AI. Today on the show, Michael Berk interviews Edouard d’Archimbaud, co-founder of Kili Technology, a Training Data Platform that turns raw, unstructured data to high-quality training data, at scale. Enjoy this engaging conversation about building AI responsibly on a foundation of good data. In this episode…
1. What is cautious AI and trustworthy AI? 2. Defining clean data vs. diverse data 3. Image classification 4. How was Kili founded? 5. How does the labeler and model work together? 6. Algorithms and model structures 7. Scaling with large data sets

Sponsors* Top End Devs * Coaching | Top End Devs

Links* Kili Technology * Blog posts * LinkedIn: Edouard d'Archimbaud

Jesse Langford spent the first half of his career as a golf instructor before pivoting to software engineering. Today on the show, Ben interviews Jesse to learn why and how he made this pivot, plus relevant career advice for all developers. Specific topics include taking ownership of your work, being comfortable making mistakes, and how to stretch yourself every day.

In this episode…
1. Taking ownership 2. Being comfortable taking risks and making mistakes 3. From back end to front end development 4. Self taught and self paced 5. Dunning-Kruger effect 6. Continually growing and learning

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Links* The Most Important Thing I Did to Become a Senior Developer * Jesse Langford * LinkedIn: Jesse Langford

When developing ML models, defining and selecting the model architecture will be fundamental to ensure the best possible outcomes. Parameters that define the model architecture are referred to as hyperparameters and the process of searching for the ideal model architecture is referred to as hyperparameter tuning. Today on the show, Ben and Michael discuss hyperparameter tuning and how to implement this into your ML modeling.

In this episode…
1. Why do we tune? 2. Optimizing the models 3. Hyperparameter tuning 4. Steps for tuning 5. Data splits 6. Linear based models 7. How do you know when you know enough? 8. Basic rules of thumb 9. Buffer in time for spikes 10. Grid searching and automation

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Enjoy this engaging AMA conversation with Michael Berk asking Ben Wilson various questions related to industry, strategy, and approaches in data science and ML engineering.In this episode…1. Why should people trust you? 2. What will you lose by hearing about other people’s failures vs. personally failing to learn? 3. How do you view the current industry? 4. Do you think data scientists and ML engineers are overpaid? 5. What are 3 things important to ROI? 6. How do you set the tone for culture?

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Links* Machine Learning Engineering in Action

Ben and Michael interview Maciej Balawejder, a mechanical engineering student passionate about AI, ML, and robotics. As an active contributor on Medium.com, Maciej has already made significant contributions to the AI and ML communities. On the show, they discuss Maciej’s recent article about optimizers in Machine Learning, plus their personal philosophies and approaches to deep learning.

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Links* Maciej Balawejder - Medium * Optimizers in Machine Learning

After ensuring your data has surpassed the hyper parameter tuning phase, what is the next step in your EDA protocol? Today on the show, Ben and Michael continue the discussion on EDA methodology within Machine Learning and discuss linear regression with OLS, decision trees, and common visualization tools for data scientists.

In this episode...

Linear regression with OLS
Accuracy metrics
Decision trees
Shapley values
Common visualization tools

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EDA is primarily used in machine learning to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for data analysis are appropriate. Today on the show, Ben and Michael discuss how to use EDA in machine learning models. In this episode... What is EDA? Tips and Tricks and steps for EDA How to approach downsampling Understanding feature sets relative to your labels Optimizing models Motivating yourself to get into the data Tools for EDA A few scenarios for discussion What is the most detrimental EDA mistake for ML Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

MLlib is Apache Spark's scalable machine learning library. Today, Ben and Michael discuss the ease of use, performance, algorithms, and utilities included in this library and how to execute the best ML workflow with MLlib. In this episode... Why stick with Spark libraries vs. a single node operation? What algorithms are not in Spark Lib? What is the min. package set to use for supervised learning? Modeling and validation Down-sampling your data MLlib vs. scikit-learn Resources Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Links MLlib | Apache Spark (https://spark.apache.org/mllib/) What is PySpark? | Domino Data Science Dictionary (https://www.dominodatalab.com/data-science-dictionary/pyspark#:~:text=PySpark%20is%20the%20Python%20API,more%20scalable%20analyses%20and%20pipelines) UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/index.php)

Apache Spark is a lightning-fast unified analytics engine for large-scale data processing and machine learning. In this episode, Ben and Michael unpack Spark by ping-ponging questions and answers, supplemented by various examples applicable to machine learning workflows. In this Episode… How does Spark work? What makes Apache Spark effective? Dot repartition in Spark Parallel processing systems What is an aggregation in Spark sequel? Analytics with Spark What is MPP? Testing for production Spark algorithms Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

Ben and Michael walk through two different cases studies relative to production ML infrastructure and recommendation engines. The first is about a free on-line tutoring service for underserved communities called “Learn to Be”, and the second centers around the online course provider “Coursera”. Ben and Michael set up the case studies with fundamental problem statements, followed by their various approaches to executing the objectives to achieve the desired process outcomes. Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

Ben interviews Michael Griffiths, Director of Data Science at ASAPP, a company leveraging AI and ML to augment and automate human work, improve operational efficiencies and customer experiences, and ultimately empower people to be their best. Michael shares specific examples of how this can be done for human agent productivity within contact centers. They also discuss fully human controlled vs automated systems, delivering value with AI and ML, and the future of AI driven technology. In this Episode… How do you deliver value with AI and ML? Fully human controlled vs. fully automated systems ML software engineering vs traditional software Using static training data models and data validation Communicating process improvements and failures What is the future of ASAPP and AI driven technology? Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Links How a Level System can Help Forecast AI Costs - KDnuggets (https://www.kdnuggets.com/2022/03/level-system-help-forecast-ai-costs.html) AI Research - ASAPP (https://www.asapp.com/ai-research/) ASAPP (https://www.asapp.com/) Special Guest: Michael Griffiths.

AutoML (automated machine learning) has become a hot topic over the past few years. Abid Ali Awan joins the show to share his approach to AutoML, when and how to utilize it compared to classic approaches. Ben and Abid also discuss open-source vs. proprietary platforms. What is AutoML? Automated machine learning provides methods and processes to make machine learning available for non-machine learning experts, to improve efficiency of machine learning and to accelerate research on machine learning. 2 levels of implementation: Blackbox AutoML can do one, or all of the things for feature selection with a statistical outset and self optimizing outcome. Whitebox AutoML exposes the code to explain how it behaves and allows you to produce predictions as to the influencing variables. Leveraging open-source toolkits vs. proprietary “The output is the model, the input is the data, and you can use that model to predict anything according to your business problem.” - Abid Ali Awan Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Links H2O.ai (https://h2o.ai/) Special Guest: Abid Ali Awan .

Video is considered the most complicated data to process and the volumes of video production are growing from day to day. Ben and Michael talk with Oleg and Anastasiya about how to leverage robotics and advanced cognitive computing-based video processing algorithms to automate the most routine parts of editing and post-production. Specifically, they discuss American sports such as the NFL, NBA, and NHL, and how to use AI to automate sports highlight reels can automate content post-processing video analytics to save time and streamline employee workflows. This is an exciting video you won’t want to miss! Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Special Guest: Anastasiya Vishnevskaya.

Michael and Ben talk about how to pick extra projects to build up your resume and become recognized as more of an expert. They discuss the specific ways to contribute within the community and who to interact with to strengthen your resume if you're new. Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

Machine learning is getting bigger by the second, so it’s good to know how to leverage it. In this episode, Michael asks Ben hypothetical questions around how to effectively deploy machine learning in multiple fields, including the stock market. In This Episode 1) How to get started in the stock market without having gobs of hedge fund money 2) The ONE thing you NEED to know before even starting a project in this space (or you’re wasting your time AND money) 3) The BEST way to leverage open source software and have a competitive advantage Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

What happens when you teach ML and data science to kids? You learn a whole lot, too. In this episode, Ben and Michael sit down with Kathryn, a prolific writer and author who simplifies advanced concepts for kids to foster their passion for science. “I just love how curious kids are. I really connect with the questions they ask and how curious they are, so that’s why I love writing for them.” - Kathryn Hulick In This Episode 1) What you NEED to remember when trying to teach complicated topics to kids 2) The BIGGEST misconception around AI that kids need to understand 3) How we can approach ML and data science to learn better (and how kids already do this!) Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Special Guest: Kathryn Hulick.

Even an amazing algorithm can’t fix communication problems. In this episode, Ben and Michael sit down with Joe Reis, a data scientist and ML developer who’s passionate about helping people level up their communication and build solid business infrastructure. “I feel like the infrastructure piece is getting better. Once you get past the technical layer, it’s about basic things like communication, no matter how much money is thrown into it.” - Joe Reis In This Episode 1) SUPER exciting trends for data science for the first time in 10+ years 2) Why you NEED to have a marketing-focused approach in the ML space nowadays 3) The BIGGEST things you should consider when hiring and scaling to keep you business infrastructure intact Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Links Joe Reis ( JoeReis ) (https://github.com/JoeReis)

Ever feel like you can’t see the forest through the trees? We get it. In this episode, Michael sits down with Maria Zentsova, an ML developer and data analyst who teaches us how to get a handle on our data. “We all know that more data leads to more accuracy, so it’s important to get hands on.” - Maria Zentsova In This Episode 1) What KDNuggets is making data science and machine learning easier this year 2) The Do’s and Don’ts of data feeds and analysis in 2022 3) Why understanding this ONE issue is the key to avoiding relevance issues and finding your way out of the weeds Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Links Build a Serverless News Data Pipeline using ML on AWS Cloud - KDnuggets (https://www.kdnuggets.com/2021/11/build-serverless-news-data-pipeline-ml-aws-cloud.html) Special Guest: Maria Zentsova.

In this episode, Ben and Michael cover more of Shreya Shankar’s deep dive into ML monitoring, including the biggest production challenges, what you NEED to know about adversarial attacks, and how to conduct effective tests and never make past mistakes again. In This Episode 1) The BIGGEST production challenges when it comes to ML in 2022 2) What you NEED to know about adversarial attacks (and how to keep your model secure) 3) How to know if your model tests are actually preventing past mistakes Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

If you’re feeling a little nervous about your baby leaving the nest, we get it. In this episode, Ben and Daniel talk with Abhilash Pattnaik, where they discuss the ONE fact about ML you can’t forget, the do’s and don’ts about applying alerts, and the often-forgotten truth about data drift. “Machine learning models are not static; they are dynamic.” - Abhilash Pattnaik In This Episode: 1) This one UNFORGETTABLE fact for anyone entering the machine learning world (especially if you have little experience) 2) What you NEED to remember about how to measure data accuracy and relevance 3) The Do’s and Don’ts of applying alerts within your models (and how NOT to make it problematic) 4) The often-ignored truth around data drift and why companies react well or poorly Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Links Censius (https://censius.ai/) Special Guest: Abhilash Pattnaik.

Ready to dive DEEP into predictive modeling? You’ve come to the right podcast. In this episode, Ben and Michael sit down with Maarit Widmann, a data scientist whose bread and butter is making models more accurate. They discuss how to effectively use confusion matrices and other tools, why you need to avoid THIS misconception to get accurate churn rates, and the BIG question you should be asking if your data seems off. “If your model is failing, it’s probably been adding up for two weeks already. Instead of monitoring the accuracy, monitor the features. Ask ‘is the data changing?’” - Maarit Widmann In This Episode 1) The THREE techniques to help simplify these advanced concepts for beginners in the ML space (and why they need to know this stuff!) 2) Why you need to remember THIS misconception to avoid getting inaccurate churn rates from your models 3) How to effectively use confusion matrices, down sampling, and other popular tools for your models (and the biggest mistakes developers are making today) 4) The REAL question you need to ask yourself if your data seems off (and how this question helps prevent fraud!) Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Special Guest: Maarit Widmann.

Mo’ advancements mean mo’ problems, and today, Michael and Ben are diving into the biggest issues of ML monitoring in 2022. They lay out the ELEVEN (cause nothing good comes easy) potential pitfalls that you should know this year, the important questions that you NEED to ask yourself before launching your baby, and this ONE phenomenon that reveals a fundamental flaw in models versus the real deal. In This Episode The checklist of ELEVEN problems with ML monitoring that you need to consider in 2022 Why THIS is inevitable with ML monitoring no matter what (and why it’s not the end of the world…unless you make it) Before you make an assumptions about your model, ask yourself THESE questions and tread carefully This ONE phenomenon that reveals a HUGE flaw in models vs. real world Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

If you’re looking for a team that actually cares about the issues you’re facing, look no further than Databricks, and they’ve got something exciting out. In this episode, Michael and Ben welcome on the development team of MLflow, an open-source lifecycle manager for machine learning. They cover how Databricks is redefining how developers and engineers collaborate, the reason behind Databricks’ crazy success, and the number ONE most important testing structure for any development team. “A lot of the success was attributed to process and dedicated focus on the interface, understanding what major problems we were going after. ” - Corey Zumar In This Episode How Databricks allows data analysis, engineers, and developers to collaborate effectively Why Databricks was able to rake in 800,000 downloads per MONTH in their first year A simple but powerful methodology that helps Databrick identify the highest ROI problems to tackle (not just the most popular ones) The number one MOST important testing structure that reveals how Databricks keeps their work top-notch What makes Databricks unique from everyone else and is the KEY to putting users first in 2022 Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Special Guests: Corey Zumar, Harutaka Kawamura, Weichen Xu, and Zhang Jin.

What do cows and camels have to do with the human brain? The latest developments in machine learning, of course! In this episode, Michael and Ben dive into a new white paper from Facebook AI researchers that reveals a LOT about the future of modeling. They discuss “cows and camels”, the question of predictive vs causal modeling, and how algorithms are getting scary good at emulating the human brain these days. In This Episode Why Facebook’s new research is VERY exciting for AI learning and causality (but what does it have to do with cows and camels?) The answer to “Is predictive or causal modeling more accurate?” (and why it’s not the best question to ask) Not sure if you need machine learning or just plain data modeling? Michael lays it out for you What algorithms are learning about human behavior to accurately emulate the human brain in 2022 and beyond Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

Want to know how businesses are measuring their success nowadays? In this episode, Ben talks with Michael Berk about how businesses are leveraging numerous performance metrics to stay on top of their game. The two emphasize what your FIRST step should be, why A/B testing is not just for marketers, and the “elephant in the room” that determines how KPIs should be integrated. “It’s the chicken or the egg problem. It’s important to think about what model you’ll potentially use as you build out these pipelines. Step 1 is defining the business objective.” - Michael Berk In This Episode: Before you focus on the pipeline or the model, Michael warns you to put THIS first on your checklist Why A/B testing is SO powerful and not just for marketers The #1 takeaway that will change how you implement KPIs Michael hammers home why “your data is only as good as your model” and why there’s no shortcut around it The “elephant in the room” that determines the what, who, and how of data analysis in any business

Michael and Ben talk about Prediction Intervals. They discuss how to put intervals around your predictions and then aiming for a certain level of confidence. Michael and Ben discuss how to put it together and how to use it. Panel Ben Wilson Michael Berk Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

Get Lifetime Access to Mani's Entrepreneurship Pack and Book Club. Use coupon code "GREAT"

Mani has summarized hundreds of business books that outline how to build, grow, and operate a business and he shares his expertise with Chuck and the listeners in this special episode.

Chuck and Mani discuss what it takes to be a successful entrepreneur. They talk about their businesses on a regular basis and Chuck's been getting a lot of requests for entrepreneurship help.

He and Mani talk about the 3 primary things that add momentum to your business and help you keep the momentum up when setbacks come your way.

Get Lifetime Access to Mani's Entrepreneurship Pack and Book Club. Use coupon code "GREAT"

Special Guest: Mani Vaya.

Ben and Chuck put their areas of expertise together to discuss how you could build Machine Learning into web and mobile applications.

They discuss the various ways data scientists and Machine Learning engineers can use their expertise to build engines and provide data and results to web and mobile developers.

Panel * Ben Wilson * Charles Max Wood

Sponsors * Top End Devs * Coaching | Top End Devs

Picks * Ben- Frank Herbert's Dune Saga 3-Book * Charles- The Well of Ascension * Charles- Traffic Secrets * Charles- Steampunk Rally Fusion * Charles- Author | Top End Devs

Roxley Steampunk Rally Fusion Deluxe Edition and 2020 Promo Pack

Traffic Secrets: The Underground Playbook for Filling Your Websites and Funnels with Your Dream Customers

The Well of Ascension: Mistborn, Book 2

Frank Herbert's Dune Saga 3-Book Boxed Set: Dune, Dune Messiah, and Children of Dune

Get the Black Friday/Cyber Monday "Double Your Productivity by 5pm Today" Deal Coupon Code: "DEEP" for a GIANT discount Mani provides us with strategies and tactics to get Deep Work time and how to get our minds into that focused state for hours at a time.

He has read hundreds of books that have taught him the secrets to getting more done by getting into this state.

He starts by telling us how he was passed over for a promotion at Qualcomm in favor of someone younger and less experienced and how that inspired him to figure out what the other guy was doing differently. He learned that he needed to get more done with the time he was spending on his projects.

The trick? Deep Work!

Deep Work is the ability to spend uninterrupted, focused time on a task to bend your entire mind toward the goal.

Other developers call it "Flow" or "the Zone."

Mani provides us with strategies and tactics to get Deep Work time and how to get our minds into that focused state for hours at a time.

Get the Black Friday/Cyber Monday "Double Your Productivity by 5pm Today" Deal Coupon Code: "DEEP" for a GIANT discount

Aliaksei Mikhailiuk joins the Adventure to discuss the tools he wishes he'd mastered before getting his PhD in Machine Learning.

However, the conversation meanders through his background, what qualifications people should have, algorithms, and approaches to machine learning along with the tools discussion.

Panel * Ben Wilson * Charles Max Wood * Michael Berk

Guest * Aliaksei Mikhailiuk

Sponsors * Top End Devs * Coaching | Top End Devs

Links * Nine Tools I Wish I Mastered Before My PhD in Machine Learning * Aliaksei Mikhailiuk * Aliaksei Mikhailiuk - Medium * LinkedIn: Aliaksei Mikhailiuk * Twitter: Aliaksei Mikhailiuk,PhD ( @mikhailiuka )

Picks * Aliaksei- The Double Descent Hypothesis * Ben- Aliaksei Mikhailiuk - Medium * Charles- Shadow Hunters | Board Game | BoardGameGeek * Charles- Author | Top End Devs * Charles- Xero * Charles- Stripe * Michael- Increase track pad or mouse speed to the Maximum

Special Guest: Aliaksei Mikhailiuk.

Michael Berk joins the adventure to discuss how he uses Machine Learning within the context of A/B testing features within applications and how to know when you have a viable test option for your setup.

Panel * Ben Wilson * Charles Max Wood

Guest * Michael Berk

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv

Links * How to Find Weaknesses in your Machine Learning Models * LinkedIn: Michael Berk * Michael Berk - Medium

Picks * Ben- David Thorne Books * Charles- Shadow Hunter * Charles- Top End Devs * Charles- Become an Author | Top End Devs * Charles- Coaching | Top End Devs * Michael- Stuart Russell

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Special Guest: Michael Berk.

Pier Paolo Ippolito joins the adventure to discuss some of the paradoxes or counterintuitive generalizations people make about their datasets. Ben, Chuck, and Pier dive into how to look at statistical data and how to identify trends in the data.

Panel * Ben Wilson * Charles Max Wood

Guest * Pier Paolo Ippolito

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv

Links * Paradoxes in Data Science * Pier Paolo Ippolito * Twitter: Pier Paolo Ippolito( @Pier_Paolo_28 )

Picks * Ben- Python Type Checking * Charles- The 360 Degree Leader * Charles- Top End Devs * Charles- Become An Author | Top End Devs * Charles- Coaching | Top End Devs * Charles- The Search for Planet X

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Special Guest: Pier Paolo Ippolito.

The Search for Planet X

The 360 Degree Leader: Developing Your Influence from Anywhere in the Organization

The panel jumps in to attempt to break your mental build regarding testing your ML Ops. They advocate for good testing practices around your code and systems and discuss how you can reliable test the various parts of your applications including your Machine Learning models.

Panel * Ben Wilson * Charles Max Wood * Francois Bertrand

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv

Picks * Ben- The Boston Housing Dataset * Charles- Coaching | Top End Devs * Charles- The 360 Degree Leader * Charles- The Laws of Wealth: Psychology and the Secret to Investing Success * Francois- Dune

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

The Laws of Wealth: Psychology and the secret to investing success

The 360 Degree Leader: Developing Your Influence from Anywhere in the Organization

Ahmad Mustafa Anis joins the adventure to discuss how he deploys his Machine Learning models using FastAPI. FastAPI is a system for connecting and running Python programs. If your model is built in Python, you can use FastAPI to deploy to Heroku or similar services.

Panel * Ben Wilson * Charles Max Wood * Francois Bertrand

Guest * Ahmad Mustafa Anis

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv

Links * How to deploy Machine Learning/Deep Learning models to the web * Machine Learning Guide Podcast * Ahmad Anis, Author at cnvrg * Ahmad Anis - KDnuggets * LinkedIn: Ahmad Anis * GitHub: Ahmad Mustafa Anis ( ahmadmustafaanis ) * Twitter: Ahmad Mustafa Anis ( @AhmadMustafaAn1 )

Picks * Ahmad- Deep Learning for Coders with Fastai and PyTorch * Ben- Arcadia: A Novel * Charles- JSJ 278 Machine Learning with Tyler Renelle * Charles- X: Multiply Your God-Given Potential * Charles- The Art of Impossible: A Peak Performance Primer * Charles- Lost Ruins of Arnak * Charles- Steampunk Rally * Charles- Gods Love Dinosaurs * Francois- Will and Vision: How Latecomers Grow to Dominate Markets

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Special Guest: Ahmad Mustafa Anis.

CGE Czech Games Edition Lost Ruins of Arnak

Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD

Will & Vision: How Latecomers Grow to Dominate Markets

X: Multiply Your God-Given Potential

The Art of Impossible: A Peak Performance Primer

Arcadia: A Novel

Emeli Dral and Elena Samuylova joins the Adventure to discuss the tools they have built to monitor data quality to determine where data problems occur, what they mean, and how to fix them.

Panel * Ben Wilson * Francois Bertrand

Guest * Elena Samuylova * Emeli Dral

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv * PodcastBootcamp.io

Links * Evidently AI * When to Retrain an Machine Learning Model? Run these 5 checks to decide on the schedule * GitHub | evidentlyai/evidently * LinkedIn: Elena Samuylova * Twitter: Elena Samuylova ( @elenasamuylova ) * LinkedIn: Emeli Dral * Twitter: Emeli Dral ( @EmeliDral )

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Special Guests: Elena Samuylova and Emeli Dral.

Demetrios Brinkmann joins the adventure to discuss how he build and supports the MLOps Slack community and online meetups. He goes into the community, moderation, running meetups, sponsorships, and much more.

Panel * Ben Wilson * Charles Max Wood * Francois Bertrand

Guest * Demetrios Brinkmann

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv * PodcastBootcamp.io

Links * MLOps Community * Twitter: mlopscommunity ( @mlopscommunity ) * LinkedIn: Demetrios Brinkmann * Twitter: Demetrios ( @Dpbrinkm )

Picks * Ben- MLFlow * Charles- Tribe of Millionaires * Charles- Top End Devs * Demetrios- Make Noise: A Creator's Guide to Podcasting and Great Audio Storytelling * Demetrios- Out on the Wire * Demetrios- Radiolab: Podcasts

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Special Guest: Demetrios Brinkmann.

Out on the Wire: The Storytelling Secrets of the New Masters of Radio

Tribe of Millionaires: What if one choice could change everything?

Make Noise: A Creator's Guide to Podcasting and Great Audio Storytelling

Conor Murphy joins the adventure to explain how he approaches new problems from customers at databricks and how he helps customers see their way past issues with their current solutions to get the outcomes they want.

Panel * Ben Wilson * Charles Max Wood * Francois Bertrand

Guest * Conor Murphy

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv * PodcastBootcamp.io

Links * A dose of awe from the bleeding edge of neuroscience, AI and philosophy * LinkedIn: Conor B. Murphy * Instagram: Conor B Murphy ( conorbmurphy )

Picks * Ben- ArjanCodes - YouTube * Charles- PodcastBootcamp.io * Charles- Masters of Doom * Charles- How to Make Sh*t Happen * Charles- Tribe of Millionaires * Conor- The Art of Impossible * Charles- The Road Back to You * Conor- Scale * Francois- Favro

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Special Guest: Conor Murphy.

The Road Back to You: An Enneagram Journey to Self-Discovery

How to Make Sh*t Happen: Make more money, get in better shape, create epic relationships and control your life!

The Art of Impossible: A Peak Performance Primer

Tribe of Millionaires: What if one choice could change everything?

Scale: The Universal Laws of Life and Death in Organisms, Cities and Companies

Masters of Doom: How Two Guys Created an Empire and Transformed Pop Culture

Antonio Alegria is the head of AI at Outsystems. He leads the effort to find ways to use AI to augment people's experience building software. He joins in to talk about how Outsystems approaches exploring and implementing
AI to make the lifecycle of software development easier.

Panel * Ben Wilson * Francois Bertrand

Guest * Antonio Alegria

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv * PodcastBootcamp.io

Links * A.I., Machine Learning Job Opportunities: What the Experts Think * AI In Code Series: OutSystems - AI drives developer navigation, automation & validation * The Role of AI Within Low-Code Development * How to Become an AI Driven Organization + Using Prebuilt AI Tools to Get Started | OutSystems * Outsystems: Revolutionizing App Development for the Enterprise * António Alegria, Outsystems | Outsystems NextStep 2020 - YouTube * Augmenting The Work of OutSystems Developers With AI-Assisted Development * OutSystems Update: AI and Machine Learning for Application Development * LinkedIn: OutSystems * Twitter: OutSystems ( @OutSystems ) * LinkedIn: Antonio Alegria * Twitter: Antonio Alegria ( @antonioalegria )

Picks * Antonio- Tesla AI Day * Ben- ZenML * Francois- 7 habits of highly effective people

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Special Guest: Antonio Alegria.

The 7 Habits of Highly Effective People: Guided Journal: Collector's Edition

Slater Victoroff joins the Adventure to discuss mutli-modal AI and machine teaching with the panel. He starts out explaining what multi-modal AI is and how it works. The conversation goes deep before veering into Machine Teaching.

Panel * Ben Wilson * Francois Bertrand

Guest * Slater Victoroff

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv * PodcastBootcamp.io

Links * LinkedIn: Slater Victoroff * Twitter: Slater Victoroff ( @sl8rv )

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Special Guest: Slater Victoroff.

Sydney Lai joins the Adventure to discuss how she and her colleagues build AI assisted features for developers and how that they handle scenarios that they can't always plan for.

Panel * Ben Wilson * Charles Max Wood

Guest * Sydney Lai

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv * PodcastBootcamp.io

Links * Developer Community | OutSystems * Software Downloads | OutSystems * Twitter: Sydney Lai ( @sydneylai )

Picks * Ben- Veritasium * Charles- PodcastBootcamp.io * Charles- Top End Devs * Charles- The Chosen * Sydney- Michael Pollan

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Special Guest: Sydney Lai .

Sandeep Uttamchandani joins the Adventure to discuss the relationships between Data Science and Machine Learning.

He walks through the ways you should set up, manage, and consider the data you use to build and train your Machine Learning systems to get the outcomes that you want.

Panel * Ben Wilson * Charles Max Wood

Guest * Sandeep Uttamchandani

Sponsors * Dev Influencers Accelerator * Level Up | Devchat.tv

Links * 98 things that can go wrong in an ML project * The Self-Service Data Roadmap * How to build a unicorn AI team without unicorns * For Successful AI Projects, Celebrate Your Graveyard * Data for Humanity * For successful AI projects, celebrate your graveyard and be prepared to fail fast * Sandeep Uttamchandani - Medium * Sandeep Uttamchandani, Ph.D. * Twitter: Sandeep Uttamchandan ( @sandeepu )

Picks * Ben- The Self-Service Data Roadmap * Charles- Level Up | Devchat.tv * Charles- Big Rock Candy Mountain Resort

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Special Guest: Sandeep Uttamchandani.

The Self-Service Data Roadmap: Democratize Data and Reduce Time to Insight

Alexey Grigorev joins the Adventure to discuss how software engineers can begin making the transition from Software Engineer to Data Scientist in their career.

Ben Wilson also gets asked about this frequently and chimes in with his experience.

Panel * Charles Max Wood * Ben Wilson * Francois Bertrand

Guest * Alexey Grigorev

Sponsors * Dev Influencers Accelerator

Links * From Software Engineering to Machine Learning * Machine Learning Zoomcamp * Datatalks.Club * LinkedIn: Alexey Grigorev * Twitter: Alexey Grigorev ( @Al_Grigor )

Picks * Alexey- Running from Complexity * Ben- Machine Learning Bookcamp * Charles- Devchat.tv/levelup * Francois- Cozy Fall Coffee Shop Ambience - YouTube

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Special Guest: Alexey Grigorev.

Ken Youens-Clark joins the adventure to discuss how to write well factored code with tests to help ML be more approachable. He, Ben, and Chuck discuss what it takes to write good code that runs efficiently, is easy to maintain, and still get ML work done.

Panel * Ben Wilson * Charles Max Wood

Guest * Ken Youens-Clark

Links * GitHub: Ken Youens-Clark ( kyclark ) * Twitter: Ken Youens-Clark ( @kycl4rk )

Picks * Ben- David Thorne * Charles- Xero * Charles- Fireside * Charles- Devchat.tv/levelup * Ken- Meditation and Mindfulness

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Special Guest: Ken Youens-Clark.

Mark Ryan is our first returning guest to the Adventure. He has created a video series for Manning showing how to use Machine Learning for Tabular data.

He walks Ben Wilson through the ins and outs of applying Deep Learning to tabular data sets and the how to find instances where this practice might be the right solution.

Panel * Ben Wilson

Guest * Mark Ryan

Sponsors * Dev Influencers Accelerator

Links * Deep Learning with Structured Data | Manning * Prepare Tabular Data | Manning * Mark Ryan - YouTube * GitHub: Mark Ryan ( ryanmark1867 ) * Twitter: Mark Ryan ( @MarkRyanMkm )

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Special Guest: Mark Ryan.

Ekrem Aksoy joins the adventure to discuss transformers and the method of helping Machine Learning algorithms focus on the important parts of an image to determine what to do.

Panel * Ben Wilson * Charles Max Wood * Daniel Svoboda * Francois Bertrand

Guest * Ekrem Aksoy

Sponsors * Dev Influencers Accelerator

Links * Attention to Transformers * Attention in the Human Brain and Its Applications in ML * See, Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving * Ekrem Aksoy - Medium * Ekrem Aksoy, PhD - Gradient * LinkedIn: Ekrem Aksoy

Picks * Ben- Read all the blog posts in this episode * Charles- Accounting software | Xero * Charles- The Prosperous Coach * Daniel- Debt - Updated and Expanded: The First 5,000 Years * Ekrem- The Book of Why: The New Science of Cause and Effect * Ekrem- Attention in Psychology, Neuroscience, and Machine Learning

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

The Book of Why: The New Science of Cause and Effect

The Prosperous Coach: Increase Income and Impact for You and Your Clients

Debt: The First 5,000 Years,Updated and Expanded

Ben Wilson explains the recent developments at DataBricks with their Machine Learning mentorship program for some of their experts. He talks about his approach to helping the Data Scientists and Developers he's mentoring to understand Machine Learning more deeply and gives advice on how others could and should drive their career forward with Machine Learning.

Panel * Ben Wilson * Charles Max Wood * Daniel Svoboda

Sponsors * Dev Influencers Accelerator

Picks * Ben- Exploring HD music on Amazon Prime * Charles- 50 Grilled Cheese * Charles- The Prosperous Coach * Charles- Kajabi * Daniel- Debt - Updated and Expanded: The First 5,000 Years

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

The Prosperous Coach: Increase Income and Impact for You and Your Clients

Debt: The First 5,000 Years,Updated and Expanded

Laszlo Sragner joins the adventure to discuss how to make your machine learning approach production ready. The discussion ranges through code quality and how to build and manage your models to keep them production ready and delivering the outcomes you're looking for.

Panel * Ben Wilson * Charles Max Wood * Francois Bertrand

Guest * Laszlo Sragner

Sponsors * Dev Influencers Accelerator

Links * Laszlo's Newsletter * How to solve Machine Learning problems for production? (Part 1) * How to solve Machine Learning problems for production? (Part 2) * Need for Speed: Why High Quality Code Matters in Data Science * LinkedIn: Laszlo Sragner

Picks * Ben- Laszlo's Newsletter * Charles- The Ruthless Elimination of Hurry * Charles- Atlas Shrugged * Charles- Oathbringer * Charles- Strava * Francois- Audible * Laszlo- Software Engineering at Google * Laszlo- Move Fast and Break Things

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Atlas Shrugged (Centennial Ed.)

The Ruthless Elimination of Hurry: How to Stay Emotionally Healthy and Spiritually Alive in the Chaos of the Modern World

Software Engineering at Google: Lessons Learned from Programming Over Time

Oathbringer: Book Three of the Stormlight Archive (The Stormlight Archive, 3)

Move Fast and Break Things: How Facebook, Google, and Amazon Cornered Culture and Undermined Democracy

Ville Tuulos is a former Netflix data scientist and engineer who now helps people manage their data pipelines. He's the author of Effective Data Science Infrastructure from Manning publishing and the creator of the Metaflow system for managing data pipelines.

He explains how to think about data and how to plan out how to gather, manage, and transform your data using a system like Metaflow.

Panel * Ben Wilson * Charles Max Wood * Francois Bertrand

Guest * Ville Tuulos

Sponsors * Dev Influencers Accelerator

Links * Metaflow * LinkedIn: Ville Tuulos * Twitter: Ville Tuulos ( @vtuulos )

Picks * Ben- Databricks Feature Store * Charles- Atlas Shrugged * Charles- 2 C clamps and a 2x6” board for clamping monitor arms * Charles- DevOps in BioInformatics with Jillian Rowe – DevOps 074 | Devchat.tv * Francois- Lucidchart * Ville- Weather API

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Atlas Shrugged (Centennial Ed.)

The conversation starts out asking what’s coming down the pipeline for the Machine Learning community. Ben explains why it’s hard to predict and leads the conversation into what the challenges really are in Machine Learning and the movements across the field to make it more clear on how to get value from your ML setup.

Panel * Ben Wilson * Charles Max Wood * Francois Bertrand

Sponsors * Dev Influencers Accelerator

Picks * Ben- Optuna * Ben- GitHub | optuna/optuna * Charles- DigitalOcean * Charles- Napoleon Hill's Outwitting the Devil * Charles- Dev Influencers | Devchat.tv * Francois- Discord

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Outwitting the Devil: The Secret to Freedom and Success (Official Publication of the Napoleon Hill Foundation)

In this episode, Ben, Francois and Chuck talk about the skills and knowledge that will help you get started with machine learning. Ben outlines 3 different things that will get you started faster than anything else. Francois and Chuck add a couple more things and they discuss the best ways to implement each one of these skills or tactics to become a top notch Machine Learning Engineer.

Panel * Ben Wilson * Charles Wood * Francois Bertrand

Sponsors * Dev Influencers Accelarator

Picks * Ben- Evidently AI * Charles- Psycho-Cybernetics * Charles- Focus Blocks * Francois- Clockify

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Psycho-Cybernetics The Search for Self-Respect

In this episode we talk with Serhii Maksymenko about how to scale video processing with DL frameworks. From buffered asynchronous processing to how to get started with projects of this complexity, Serhii discusses his project work and unique take on how to build these systems without breaking the bank.

Panel * Ben Wilson * Charles Max Wood * Francois Bertrand

Guest * Serhii Maksymenko

Sponsors * Dev Influencers Accelerator

Links * Deep Learning-based Real-time Video Processing * LinkedIn: Serhii Maksymenko

Picks * Ben- MLOps Community * Ben- Hands-on Scala Programming * Ben- Sixteen Different Flavours of Hell * Charles- Who Now How * Charles- Masters Swim Team * Charles- As a Man Thinketh * Charles- Psycho-Cybernetics * Francois- Featuretools * Francois- Comet * Serhii- Atomic Habits

Contact Ben:

Databricks
GitHub | BenWilson2/ML-Engineering
GitHub | databrickslabs/automl-toolkit
LinkedIn: Benjamin Wilson

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Contact Francois:

Francois Bertrand
GitHub | fbdesignpro/sweetviz

Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones

Hands-on Scala Programming: Learn Scala in a Practical, Project-Based Way

Sixteen Different Flavours of Hell

Who Not How: The Formula to Achieve Bigger Goals Through Accelerating Teamwork

As a Man Thinketh (Volume 1) (Classic Thoughts and Thinkers, 1)

Psycho-Cybernetics The Search for Self-Respect

Chuck dives into the 3 essentials for getting the next successful outcome you want in your career. Whether that's something simple like a raise or something more complex like going freelance, you can achieve it by working on 3 main areas.

First, building skills. The most obvious type of skills you'll need is technical skills. However, don't neglect your people skills and your organizational skills as well since you're often paid for how you work with people and enhance their work and how you put your work together in the most efficient ways.

Second, building relationships. Often other people will be able to help you find the opportunities or will be the ones to make the decisions that impact your ability to get the outcome you want. Having good relationships is key to having good outcomes.

Third, building recognition. Being known for being valuable in important ways allows you to leverage the skills you have to build better relationships and create opportunities to get what you need to get the outcomes you want by giving people what they want. A podcast is a great way to do all three. Chuck explains exactly how that works in this podcast and goes deeper as part of the Dev Influencers Accelerator.

Panel * Charles Max Wood

Chuck dives into the 3 essentials for getting the next successful outcome you want in your career. Whether that's something simple like a raise or something more complex like going freelance, you can achieve it by working on 3 main areas.

First, building skills. The most obvious type of skills you'll need is technical skills. However, don't neglect your people skills and your organizational skills as well since you're often paid for how you work with people and enhance their work and how you put your work together in the most efficient ways.

Second, building relationships. Often other people will be able to help you find the opportunities or will be the ones to make the decisions that impact your ability to get the outcome you want. Having good relationships is key to having good outcomes.

Third, building recognition. Being known for being valuable in important ways allows you to leverage the skills you have to build better relationships and create opportunities to get what you need to get the outcomes you want by giving people what they want. A podcast is a great way to do all three. Chuck explains exactly how that works in this podcast and goes deeper as part of the Dev Influencers Accelerator.

Panel * Charles Max Wood

Sponsors * Dev Influencers Accelerator

Chuck explains what he taught Nathan last week when we asked how to get hired at a FANG (Facebook Apple/Amazon Netflix Google) company. Essentially, it boils down to how to build the skills and knowledge needed to pass the interview. How to build the relationships to get into the door and have the interviewer want you to succeed. And how to build the reputation that has the company wanting you regardless of the outcome.

This approach also works for speaking at conferences, selling courses, and other outcomes as well as it's the core of building a successful career as an influencer.

Panel

Charles Max Wood

Michael Galarnyk is a Developer Relations at AnyScale and has nearly 10,000 follows on Medium. He joins the adventure to walk Chuck through how he's parallelized the training of his Machine Learning models on multi-core machines. He also walks Chuck through the ins and outs of being in Developer Relations.

Panel * Charles Max Wood

Guest * Michael Galarnyk

Sponsors * Dev Influencers Accelerator

Links * Speeding up Scikit-Learn Model Training * How to Build a Data Science Portfolio * Michael Galarnyk - Medium * Twitter: Michael Galarnyk ( @GalarnykMichael ) * LinkedIn: Michael Galarnyk

Picks * Charles- Words of Radiance: Stormlight Archive * Charles- Buying cars on local classifieds * Michael- The Cost of Financing a Car (Car Loans) * Michael- Success is an Iceberg (we all fail sometimes) * Michael- Destiny's Crucible | Olan Thorensen

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

Words of Radiance (The Stormlight Archive, Book 2) (The Stormlight Archive, 2)

Derrick Mwiti joins the adventure to discuss the various tools you can use to jumpstart your Machine Learning adventure. He walks through several frameworks for Machine Learning and points out several Tensorflow extensions that will make your Machine Learning models better and your understanding of what is going on easier.

He and Chuck walk through each one and highlight when and why you'd want to use them.

Panel * Charles Max Wood

Guest * Derrick Mwiti

Sponsors * Dev Influencers Accelerator

Links * The Best Machine Learning Frameworks & Extensions for TensorFlow * Derrick Mwiti * Derrick Mwiti | Data Scientist | Author | Mentor | Udemy * Twitter: Derrick Mwiti ( @_mwitiderrick ) * LinkedIn: Derrick Mwiti

Picks * Charles- Who Not How * Charles- Fanatical Prospecting * Charles- Hunting Hitler * Derrick- Purple Cow: Transform Your Business by Being Remarkable * Derrick- This Is Marketing: You Can't Be Seen Until You Learn to See

Contact Charles:

Devchat.tv
DevChat.tv | Facebook
Twitter: DevChat.tv ( @devchattv )

This Is Marketing: You Can't Be Seen Until You Learn to See

Who Not How: The Formula to Achieve Bigger Goals Through Accelerating Teamwork

None

Fanatical Prospecting: The Ultimate Guide to Opening Sales Conversations and Filling the Pipeline by Leveraging Social Selling, Telephone, Email, Text, and Cold Calling (Jeb Blount)

Purple Cow: Transform Your Business by Being Remarkable by Seth Godin (2003-05-12)

Qingquan Song is a member of the AutoKeras team and recent Phd graduate from Texas A&M University. He co-authored the Automated Machine Learning book from Manning publishing and joins the adventure to explain automated machine learning and how it can be used to set up and to refine machine learning models. He also dives into how to use the tools that exist to take advantage of the techniques it offers.

Panel * Charles Max Wood

Guest * Qingquan Song

Sponsors * Dev Influencers Accelerator

Links * Automated Machine Learning in Action * Haifeng Jin's Blog * Xia Ben Hu, Texas A&M University * AutoKeras * About me - Qingquan Song * LinkedIn: Qingquan Song

Picks * Charles- Who Not How * Charles- Ruby Rogues | Devchat.tv * Charles- Encourage people to have empathy * Qingquan- Working as a team is better than working individually * Qingquan- Focus on one thing at a time * Qingquan- "Work hard and Play Harder" * Qingquan- Learn from others

Who Not How: The Formula to Achieve Bigger Goals Through Accelerating Teamwork

Chuck was on a strategic call with one of his potential coaching clients talking about cryptocurrencies and realized that this is one of the major reasons that people want to become influencers. Or, rather, that many people aspire to make a difference and/or make money and the best way to do that is to become the person people go to for what you do.

So, how do you become the first person people think of when they think of that thing you know how to do? Let Chuck tell you.

Panel

Charles Max Wood

Annie Didier from NASA’s Jet Propulsion Lab talks to us about the Machine Learning algorithms and process that goes into how the next Mars rover will choose how it moves across the surface of Mars. She explains each algorithm and how they go together to make the decisions that the Rover makes.

Panel * Charles Max Wood

Guest * Annie Didier

Sponsors * Dev Influencers Accelerator

Links * MAARS - Machine Learning-based Analytics for Automated Rover Systems (Final Presentation) * SCOTI: Science Captioning of Terrain Images for data prioritization and local image search * MAARS: Machine learning-based Analytics for Automated Rover Systems * SULU: Scalable and Distributed Machine Learning Framework with Unified Encoder for Mars Rover Missions * AI4Mars - Zooniverse * LinkedIn: Annie Didier

Picks * Annie- When Breath Becomes Air * Charles- The Martian * Charles- Back Market * Charles- monday.com

When Breath Becomes Air

The Martian

Charles talks about the things that get developers stuck when they're trying to start their podcast or other influencer channel. He explains how to get around having those things hamper your journey.

Panel

Charles Max Wood

Gant's back!!! He's releasing a book with Manning Publishing about Tensorflow.js and he's here to discuss all the details with us. He explains the difference between Teonsorflow and Tensorflow.js and goes into some of the pros and cons of using it. He also explains the concepts he goes over for new ML engineers and for ML engineers learning JavaScript.

Panel * Charles Max Wood

Guest * Gant Laborde

Sponsors * Dev Influencers Accelerator

Links * Learning TensorFlow.js by Gant Laborde * Learning TensorFlow.js: Powerful Machine Learning in JavaScript by Gant Laborde * Twitter: Gant Laborde ( @GantLaborde ) * gantlaborde.com

Picks * Charles- monday.com * Charles- The Sales Development Playbook Trish Bertuzzi * Gant- Antifragile by Nassim Nicholas Taleb

Learning TensorFlow.js: Powerful Machine Learning in JavaScript

The Sales Development Playbook: Build Repeatable Pipeline and Accelerate Growth with Inside Sales

Antifragile: Things That Gain from Disorder (Incerto)

Charles Max Wood talks about how to build, grow, and benefit from positive relationships within programming. He talks about how he's built genuine positive relationships with hundreds of programmers and how he and others have grown from those relationships. He also explains that you get out of relationships what you put into them. Finally, he goes into how to begin to build relationships by building a system of influence you can use on behalf of the people you want relationships with.

Panel

Charles Max Wood

Miguel and Chuck discuss how to stay current in the rapidly changing world of Machine Learning and Artificial Intelligence. They go over how to pick books, newsletters, podcasts, and other resources to up your Machine Learning knowledge and skills.

Panel

Charles Max Wood
Miguel Morales

Sponsors

Dev Influences Accelerator

Picks

Charles- The Umbrella Academy
Charles- The Sales Development Playbook
Miguel- Deep Reinforcement Learning

The Sales Development Playbook: Build Repeatable Pipeline and Accelerate Growth with Inside Sales

Charles Max Wood discusses several opportunities that came his way early in his podcasting career and other opportunities that have come to other people after only a couple of podcast episodes. He explains why that happens and how you can use this to create more influence as a developer.

Panel

Charles Max Wood

Charles Max Wood discusses several opportunities that came his way early in his podcasting career and other opportunities that have come to other people after only a couple of podcast episodes. He explains why that happens and how you can use this to create more influence as a developer.

Panel

Charles Max Wood

Charles Max Wood started podcasting because it sounded fun and because he wanted to talk about technology. He learned pretty quickly that it got him access to people who understood the things he wanted to learn. The reasons changed over the years, as Charles explains before he talks about the big payoff he gets now from doing the podcasts.

Panel

Charles Max Wood

Francois Bertrand is the author of a tool that builds in powerful data visualization tools for datasets that allow data scientists and machine learning engineers to look at their data and analyze various qualities that they have. This could allow engineers to make qualitative calls regarding the data they use to train their models or evaluate the results they get from models after the fact. Francois explains how he built it and how to use it for these types of uses.

Panel * Charles Max Wood * Miguel Morales

Guest * Francois Bertrand

Sponsors * Dev Influencers Accelerator

Links * FRANCOIS BERTRAND | DATA VISUALIZATION DESIGNER

Picks * Charles- Dev Influencers | Devchat.tv * Charles- Teachable: Create and sell online course and teaching * Charles- Walkie Talkie App for Team Communication | Voxer * Charles- ThriveCart * Charles- ScreenFlow * Francios- Featuretools | An open source python framework for automated feature engineering * Miguel- Deep Unsupervised Learning -- Berkeley Spring 2020

Jason Weimann started out as an enthusiast of the Massively Multiplayer Online Role Playing Game, Everquest. After becoming a software developer and building a collaborative community playing the game, learn how he used his connections to get a job working for the company that made the game, even if it wasn't a job working as a game developer and how that led to a career working on one of the most popular online games of the time.

Panel

Charles Max Wood

Ather Fawaz joins the discussion to walk us through the world of qubits, quantum computers, machine learning algortithms, and what quantum computer means for machine learning. He explains the basics of quantum computer and who the major players are in the space and then explains some of the advancements people are making by scheduling time on their quantum computers.

Panel * Charles Max Wood * Miguel Morales

Guest * Ather Fawaz

Sponsors * Dev Heroes Accelerator

Picks * Ather- Learn Quantum Computation using Qiskit * Ather- Formula 1: Drive to Survive * Charles- The Circle (2017) * Miguel- Reinforcement Learning and Stochastic Optimization by Warren B. Powell

Chuck outlines how he's used his podcasts to find mentors to continue his learning journey over 12 years of podcasting. Some mentors have been long lived relationships while others have lasted only a few months or even days. This episode shares Chuck's experience learning from the top people in the development community as a programmer and podcaster.

Panel

Charles Max Wood

Chuck outlines how he's used his podcasts to find mentors to continue his learning journey over 12 years of podcasting. Some mentors have been long lived relationships while others have lasted only a few months or even days. This episode shares Chuck's experience learning from the top people in the development community as a programmer and podcaster.

Panel

Charles Max Wood

Ben Wilson is the author of Machine Learning in Action from Manning. He leads us through the process of compiling data, building algorithms, and learning Machine Learning.

Panel

Charles Max Wood

Guest

Ben Wilson

Sponsors

Dev Heroes Accelerator

Picks

Ben- DataTalks.Club
Charles- Devchat.tv | She's in Tech
Charles- Looking for sponsors for the show
Charles- Walkie Talkie App for Team Communication | Voxer

We have a new panelist! Plus, Edward Raff joins the Adventure to discuss his new book Inside Machine Learning. He walks us through Convolutional Neural Networks and then talks us through to build, train, and use them to solve problems through Machine Learning.

The conversation ranges into having good data sets, tweaking your network, and when a Convolutional Neural Network is an appropriate tool for the problem you're trying to solve.

Panel * Charles Max Wood * Miguel Morales

Guest * Edward Raff

Sponsors * Dev Heroes Accelerator

Picks * Charles- Docker * Charles- Paramount+ * Edward- Java | Oracle * Edward- Make-A-Wish America * Miguel- Full Stack Deep Learning - Spring 2021

Charles Max Wood goes into the origin story of his podcasting career and how it relates to his programming career. He starts with his interest from a young age in technology and his dreams of being a radio DJ. He moves quickly through college and into his first job after college where he was introduced to podcasts by a co-worker who had purchased an iPod.

He calls out several mentors like Gregg Pollack, Eric Berry, Nate Hopkins, Cliff Ravenscraft, David Brady, Dave Jackson, and many more.

He then explains what he'd do differently if he were starting today.

Join the Dev Heroes Accelerator at https://devchat.tv/hero

Panel

Charles Max Wood

Sponsors

Dev Heroes Accelerator

Charles Max Wood explains how he landed his first 4 freelance clients that took him through a few years of freelancing with only 3 years of experience and a few hundred podcast listeners. Funnily enough, they actually came to him, not the other way around.

He explains how he made himself attractive to them and then turned it into a mutually profitable relationship once he had their attention.

Panel

Charles Max Wood

Sponsors

Dev Heroes Accelerator

Chris explains how Tensorflow has grown over the last several years and the how it can be used to build and grow Machine Learning Systems. He explains the different algorithms you can use and the different types of problems it can solve.

Chris works at the Jet Propulsion Laboratory for NASA and dives into a few instances where Machine Learning is used at NASA.

Panel * Charles Max Wood

Guest * Chris Mattmann

Sponsors * Dev Heroes Accelerator

Links * Manning | Machine Learning with TensorFlow, Second Edition * Twitter: Chris Mattmann * GitHub | ChrisMattmann

Picks * Charles- Star Trek: Discovery * Charles- The Umbrella Academy | Netflix * Charles- Dev Heroes Accelerator | Devchat.tv * Chris- Twitter: Sincerely, Los Angeles * Chris- Sincerely, Los Angeles

Miguel Morales is a Machine Learning engineer at Lockheed Martin and teaches at Georgia Institute of Technology. This episode starts with a basic explanation of Reinforcement Learning. Miguel then talks through the various methods of implementing and training systems through Reinforcement Learning. We talk algorithms and models and much more…

Panel * Charles Max Wood

Guest * Miguel Morales

Sponsors * Dev Heroes Accelerator

Links * GitHub | mimoralea/gdrl * Manning | Grokking Deep Reinforcement Learning * Twitter: mimoralea * Email: mimoralea@gmail.com

Picks * Charles- The Expanse by James S. A. Corey * Charles- Dev Heroes Accelerator * Miguel- Deep Reinforcement Learning * Miguel- Youtube: Dimitri Bertsekas * Miguel- RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning * Miguel- Thor ( 2011 )

The Expanse Boxed Set: Leviathan Wakes, Caliban's War and Abaddon's Gate

John-Daniel Trask, founder and CEO of Raygun, talks about his experience building a monitoring company and about how to measure the speed and quality of your code.

John-Daniel Trask, founder and CEO of Raygun, talks about his experience building a monitoring company and about how to measure the speed and quality of your code.

Charles Max Wood takes a solo flight into how to make an impact on the development community and build the career you want at the same time. Chuck starts out summarizing his journey over the last year or so and then dives into his vision of how people can grow into becoming an influencer and using that to create opportunities in your life and career.

Please check out devchat.tv/nextlevel

Panel

Charles Wood

Sponsors

Next Level Mastermind

Peter Elger and Eóin Shanaghy join Charles Max Wood to dive into what Artificial Intelligence and Machine Learning related services are available for people to use. Peter and Eóin are experts in AWS and explain what is provided in its services, but easily extrapolate to other clouds. If you're trying to implement Artificial Intelligence algorithms, you may want to use or modify an algorithm already built and provided to you.

Panel * Charles Wood

Guest * Eóin Shanaghy * Peter Elger

Sponsors * Next Level Mastermind

Links * fourTheorem * Twitter: Eóin Shanaghy * Twitter: Peter Elger

Picks * Charles- The Eye of the World: Book One of The Wheel of Time by Robert Jordan * Charles - Changemakers With Jamie Atkinson * Charles- Podcast Domination Show by Luis Diaz * Charles- Buzzcast * Charles- Podcast Talent Coach * Eóin- IKEA | IDÅSEN Desk sit/stand, black/dark gray63x31 1/2 " * Eóin- Kinesis | Freestyle2 Split- Adjustable Keyboard for PC * Peter- The Wolfram Physics Project * Peter- PBS Space Time * Peter- Youtube Channel | 3Blue1Brown * Peter- Cracking the Code

The Eye of the World: Book One of The Wheel of Time (Wheel of Time, 1)

Jean-Georges Perrin compares Apache Spark to an operating system for data management. He explains how it can be used to pull data from disparate data sources, process the data, feed it into Machine Learning algorithms and stream the data out to other data streaming services.

Coupon code: podmladventure20

Panel

Charles Wood

Guest

Jean-Georges Perrin

Sponsors

Next Level Mastermind

Picks

Charles- Talk to people you don’t agree with and try to understand their point of view.
Charles- Eureka on Amazon Prime
Charles- The Expanse books and series

None

Rishal Hurbans is the author of Grokking Artificial Intelligence Algorithms. He walks us through how to learn different Machine Learning algorithms. He also then walks us through the different types of algorithms based on different natural systems and processes.

Panel * Chuck Wood

Guest * Rishal Hurbans

Links * Kaggle: Your Machine Learning and Data Science Community * Rishal Hurbans * Inktober * Book giveaway link

Picks * Chuck- Hero with a thousand faces by Joseph Campbell * Chuck- Masterbuilt smoker * Rishal-Learn something new everyday * Rishal- Building a StoryBrand by Donald Miller

The Hero with a Thousand Faces (The Collected Works of Joseph Campbell)

Building a StoryBrand: Clarify Your Message So Customers Will Listen

Masterbuilt MB20070210 Analog Electric Smoker with 3 Smoking Racks, 30 inch, Black

Ken Youens-Clark regales us with his history through Jazz, Microsoft Tech, and Python and bioinformatics. Regular expressions are a fundamental part of data identification and cleaning. Also, we touch on the importance of types and tests as a specification for yourself and others. Python, as a data-science starting point, should be clean, functional, and friendly. Aspiring data scientists can learn a lot about the importance of fundamentals and clear encodings that fit the desired needs.

Panel * Beril Sirmacek * Gant Laborde

Guest * Ken Youens-Clark

Links * Manning | Tiny Python Projects by Ken Youens-Clark * Tiny Python Project PDF by Ken Youens-Clark * Tiny Python Project by Ken Youens-Clark * ROSALIND | Problems | Locations * Twitter: Ken Youens-Clark

Picks * Beril- Youtube Channel: Beril Sirmacek * Gant- Twitter: Jason Mayes * Ken- Netflix Series: " The Queen's Gambit"

Get the 2020 Goal Setting Workshop + Success Accelerator Deal HERE (Coupon Code: GOALS for a massive discount)

Mani Vaya joins Charles Max Wood to walk him through the 6 pillars of success that lead to meeting your goals.

Mani has read thousands of books on success, setting and achieving goals, and personal growth and has distilled these 6 principles from the books and then figured out how to put them into practice.

He and Chuck walk through the principles and strategies that create success and allow you to set goals that will bring you the things you want during the next year or so.

Listen to this episode to learn how to crush your biggest goals in 2021.

Get the 2020 Goal Setting Workshop + Success Accelerator Deal HERE (Coupon Code: GOALS for a massive discount)

Alexey Grigorev is the lead data scientist for one of the biggest classified ads companies in the world. He walks us through gathering, mining, and understanding data to improve things. One big component of this is Machine Learning. It optimizes business processes and helps data scientists understand the data they have.

Panel * Beril Sirmacek * Daniel Svoboda * Gant Laborde

Guest * Alexey Grigorev

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com

Picks * Alexey- DataTalks.Club * Beril- Rise of AI | Building an European AI Ecosystem * Daniel - dair.ai- Medium * Daniel- Quantum Stat- Medium * Daniel- Towards Data Science * Gant- AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence by Laurence Moroney

AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence

Mark Ryan is the lead Data Scientist for an insurance company in Toronto. He walks us through the ins and outs of structured data, how to manage it, and how to build Machine Learning systems. His book walks the reader through building a system that predicts whether bus routes in Toronto will be late using public domain data.

Panel * Charles Max Wood

Guest * Mark Ryan

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com

Links * OpenAI * Rasa * Keras

Picks * Chuck - Podcast Growth Summit * Chuck-Most Valuable Dev * Chuck- Wheel of Time by Robert Jordan * Mark - Spitting Image

Get the Black Friday/Cyber Monday "Double Your Productivity by 5pm Today" Deal Coupon Code: "DEEP" for a GIANT discount Mani provides us with strategies and tactics to get Deep Work time and how to get our minds into that focused state for hours at a time.

He has read hundreds of books that have taught him the secrets to getting more done by getting into this state.

He starts by telling us how he was passed over for a promotion at Qualcomm in favor of someone younger and less experienced and how that inspired him to figure out what the other guy was doing differently. He learned that he needed to get more done with the time he was spending on his projects.

The trick? Deep Work!

Deep Work is the ability to spend uninterrupted, focused time on a task to bend your entire mind toward the goal.

Other developers call it "Flow" or "the Zone."

Mani provides us with strategies and tactics to get Deep Work time and how to get our minds into that focused state for hours at a time.

Get the Black Friday/Cyber Monday "Double Your Productivity by 5pm Today" Deal Coupon Code: "DEEP" for a GIANT discount

In this episode of Adventures in Machine Learning, the amazing author and course creator Frank Kane entertains our panel with information and examples. Beril Sirmacek, Gant Laborde, Daniel Svoboda, & Charles Wood talk with Frank Kane about recommender systems. The discussion elaborates on collaborative and content based recommendation systems, how they all work and how amazing they can be. Frank’s variety of experience provides fun stories, exciting examples, and a roadmap for beginners filled the complex domain with friendly stories. This episode is a MUST LISTEN for people interested in getting into Machine Learning or recommender systems.

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com * CacheFly

Panel * Charles Max Wood * Gant Laborde * Daniel Svoboda * Beril Sirmacek

Guest * Frank Kane

Links * https://gabriellecrumley.com/

Picks Daniel Svoboda:

Silicon Valley
machinelearningmastery.com

Beril Sirmacek:

XAI course 2020 ~ Module2 ~ Introduction to AI & ML

Gant Laborde:

https://mlconf.eu/

Charles Max Wood:

Stroopwafel (dutch food)
https://www.podcastgrowthsummit.co/

Frank Kane:

https://sundog-education.com/
datascience.com
Until the End of Time: Mind, Matter, and Our Search for Meaning in an Evolving Universe

Follow Adventures in Machine Learning on Twitter > @podcast_ml

Until the End of Time: Mind, Matter, and Our Search for Meaning in an Evolving Universe

Nick Chase is the author of the video series “Machine Learning for Mere Mortals.” He helps break down some of the misconceptions about how complicated Machine Learning is and the magical parts of the science. He and the panelists then dive into the basics of what you need to know and break up the scary sounding terms and mathematical concepts into bite size pieces.

Enter for a chance to win a copy of Nick’s video course at https://devchat.tv/mere-mortals.

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com * CacheFly

Panel * Charles Max Wood * Jason Mayes

Guest * Nick Chase

Picks Jason Mayes:

Browser-Based Augmented Reality Sudoku Solver using TensorFlow and Image Processing

Charles Max Wood:

https://www.brandonsanderson.com/the-stormlight-archive-series/
https://developeronfire.com/podcast/episode-448-nicholas-chase-expanding-what-people-think-they-can-do

Nick Chase:

Machine Learning for Mere Mortals

Follow Adventures in Machine Learning on Twitter > @podcast_ml

In this episode of Adventures in Machine Learning, the panelists chat with Laurence Moroney about the history of AI in the UK. We talk about the AI overlords, ethics, and the need for teaching. Industry revolutions and how we can adapt them to improve life. Laurence is working on new synthetic datasets so tune in and check it out!

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com * CacheFly

Panel * Charles Max Wood * Gant Laborde * Jason Mayes * Daniel Svoboda

Guest * Laurence Moroney

Picks Jason Mayes:

TensorFlow.js Show and Tell on Youtube

Gant Laborde:

https://www.tensorflowtictactoe.co/

Daniel Svoboda:

Youtube: Kaggle

Laurence Moroney:

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
The Manga Guide to Machine Learning

Follow Adventures in Machine Learning on Twitter > @podcast_ml

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

In this episode of Adventures in Machine Learning, Charles and Gant chat with Milecia about applying AI to UI/UX and the conversation takes a creative turn as they discuss plenty of other fun-filled and exciting topics she’s working on. There’s banter about self-driving cars and golf carts as they apply AI/ML to practical and non-practical uses. The epic mind-powered guitar takes the stage and classic movies get referenced. Be sure to follow Milecia on her adventures in Machine Learning on her Twitter at https://twitter.com/flippedcoding.

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com * CacheFly

Panel * Charles Max Wood * Gant Laborde

Guest * Milecia McGregor

Links * HalfStackNYC talk * Hawaii * https://github.com/google-research/bert * https://scholar.google.com/ * https://audible.com/code

Picks Gant Laborde:

http://virtualcoffee.io/

Charles Max Wood:

The Black Prism

Milecia McGregor:

War of Art
https://javascript-conference.com/node-js/reading-thoughts-with-javascript/

Follow Adventures in Machine Learning on Twitter > @podcast_ml

The War of Art: Winning the Inner Creative Battle

The Black Prism

In this week’s episode of Adventures in Machine Learning we have Hassan Kane, data scientist lead at Entropy Labs. Hassan discusses his journey from being raised in Ivory Coast, Africa to getting his education in computer science from MIT, to his discovery and embracing of machine learning. Hassan discusses various applications of machine learning, including that of NASA, satellites and edge devices..

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com * CacheFly

Panel * Charles Max Wood * Gant Laborde

Guest * Hassan Kane

Links * www.kaggle.com/c/birdsong-recognition

Picks Gant Laborde:

My daughter turning 2 years old
twitter.com/funmachinelearn

Charles Max Wood:

Black Prism
writingexcuses.com
blastoffbootcamper.com

Hassan Kane:

AI-Superpowers-China-Silicon-Valley
twitter.com/black_in_ai

Follow Adventures in Machine Learning on Twitter > @podcast_ml

Ai Superpowers: China, Silicon Valley, and the New World Order

The Black Prism

TensorFlow is a machine learning library that allows a user to program deep learning architectures. It is normally associated with backend programming languages like Python and is written in C++, but what if you can utilize it in Javascript to program deep learning models for frontend web applications. Guest Jason Mayes talks about doing this with Tensorflow JS.

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com * CacheFly

Panel * Charles Max Wood * Gant Laborde

Guest * Jason Mayes

Links * https://medium.com/google-developer-experts/improve-your-virtual-setup-sound-eee8c22036fc * https://github.com/jasonmayes * http://www.jasonmayes.com * https://www.linkedin.com/in/creativetech * Github Code * Google Group * codepen.io/topic/tensorflowGlitch * glitch.com/@TensorFlowJS

Picks Gant Laborde:

Beginning Machine Learning with TensorFlow.js

Charles Max Wood:

https://podcastplaybook.co
Legends (TV show)

Jason Mayes:

MadeWithTFJS

Follow Adventures in Machine Learning on Twitter > @podcast_ml

Beril Sirmacek is a data scientist and an assistant professor at Jonkoping University. She explains what computer vision is, what type of projects are done with it, and her own work regarding it.

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com * CacheFly

Panel * Charles Max Wood * Gant Laborde

Guest * Beril Sirmacek

Links * https://www.youtube.com/user/DrSirmacek/videos * http://www.berilsirmacek.com/ * Sequential image processing methods for improving semantic video segmentation algorithms * DEEPLAB vs YOLO * Understanding model predictions with LIME

Picks Gant Laborde:

React Native EU

Charles Max Wood:

Warbreaker Mass Market

Beril Sirmacek:

Fast Academic Success: Little Book of The Life Changing Secrets

Follow Adventures in Machine Learning on Twitter > @podcast_ml

Fast Academic Success: Little Book of The Life Changing Secrets (Success Principles)

Warbreaker (Sci Fi Essential Books)

Benson Ruan talks about his experiences as a machine learning tech lead for a fintech company in Sydney, Australia. He goes over his education in machine learning from Coursera, especially doing Andrew Ng’s deep learning course that started his journey up to his current position.

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com * CacheFly

Panel * Charles Max Wood * Gant Laborde

Guest * Benson Ruan

Links * Real-time webcam background change with body segmentation technique * Face mask virtual try on with face landmarks detection technique * Twitter Sentiment Analysis

Picks Gant Laborde:

My Hero Academia

Charles Max Wood:

The 12 Week Year
The Obstacle Is the Way by Ryan Holiday

Benson Ruan:

TensorFlow.js Community Show & Tell

Follow Adventures in Machine Learning on Twitter > @podcast_ml

One of the hottest fields right now in machine learning is natural language processing. Whether it’s getting sentiment from tweets, summarizing your documents, sarcasm detection, or predicting stock trends from the news, NLP is definitely the wave of the future. Special guest Daniel Svoboda talks about transfer learning and the latest developments such as BERT that promises to revolutionize NLP even further.

Sponsors * Machine Learning for Software Engineers by Educative.io * Audible.com * CacheFly

Panel * Charles Max Wood * Gant Laborde

Guest * Daniel Svoboda

Links * towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp * ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html * ai.googleblog.com/2017/08/transformer-novel-neural-network.html * www.nltk.org * spacy.io * https://www.kaggle.com

Picks Charles Max Wood:

Traffic Secrets: The Underground Playbook for Filling Your Websites and Funnels with Your Dream Customers
Range: Why Generalists Triumph in a Specialized World

Gant Laborde:

AI-QuickStartGuide.pdf

Daniel Svoboda:

Star Trek: Picard

Follow Adventures in Machine Learning on Twitter > @podcast_ml

Traffic Secrets: The Underground Playbook for Filling Your Websites and Funnels with Your Dream Customers

Range: How Generalists Triumph in a Specialized World

Implementing deep learning algorithms require knowledge of various DL libraries, how to interface outside files or streaming data to it, along with tuning all sorts of parameters. Deep Learning also does not give you much explanation on what features contribute to a model working well. Jorge Torres discusses MindsDB, a new framework for AutoML that simplifies implementation of neural network models for researchers, along with providing explanation of features.

Sponsors * Machine Learning for Software Engineers by Educative.io * CacheFly

Panel * Charles Max Wood * Gant Laborde

Guest * Jorge Torres

Links * www.mindsdb.com * pytorch.org * tensorflow.org * www.geeksforgeeks.org/confusion-matrix-machine-learning * www.pyimagesearch.com/2020/02/17/autoencoders-with-keras-tensorflow-and-deep-learning * https://www.kiwibot.com/

Picks Gant Laborde:

NVIDIA RTX Voice: Setup Guide

Charles Max Wood:

Russell Brunson’s books

Jorge Torres:

Iain M. Banks, Author on Amazon
krisp.ai

Follow Adventures in Machine Learning on Twitter > @podcast_ml

Machine learning is a complex subject that requires knowledge in many various subjects such as linear algebra, probability, algorithms, calculus, etc. But what if you could simplify machine learning to the extent that a child would be able to grasp it? Robert Plummer discusses incorporating machine learning for Javascript and opens up a whole new paradigm.

Sponsors * Machine Learning for Software Engineers by Educative.io * CacheFly

Panel * Charles Max Wood * Gant Laborde * Eric Chalmers

Guest * Robert Plummer

Links * Javascript for machine learning? Get a real language * www.learnwithjason.dev * maxcoders.io

Picks Eric Chalmers:

The problem with metrics is a big problem for AI

Gant Laborde:

Spooky Fun with Race Recognition

Charles Max Wood:

The MaxCoders Guide To Finding Your Dream Developer Job

Robert Plummer:

Abstract: The Art of Design | Netflix

Follow Adventures in Machine Learning on Twitter > @podcast_ml

The MaxCoders Guide To Finding Your Dream Developer Job

GANS are one of the revolutionary breakthroughs in machine/deep learning as they are able to create authentic facial images. One of the latest breakthroughs has been with reconstructing actual images of the Roman Emperors using their busts and frescos. Special guest Jason Antic talks about GANs and his work with deOldify, an application to color and restore images.

Sponsor * Machine Learning for Software Engineers by Educative.io * CacheFly

Panel * Charles Max Wood * Gant Laborde * Eric Chalmers

Guest * Jason Antic

Links * fast.ai * DeOldify Facebook F8 Movie Colorization Demo * TensorBoard: TensorFlow’s visualization toolkit * pytorch.org

Picks Eric Chalmers:

The Untethered Soul: The Journey Beyond Yourself

Gant Laborde:

Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners

Charles Max Wood:

The 360 Degree Leader: Developing Your Influence from Anywhere in the Organization

Jason Antic:

TV Show: Adam Ruins Everything

Follow Adventures in Machine Learning on Twitter > @podcast_ml

Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners

The Untethered Soul: The Journey Beyond Yourself

The 360 Degree Leader: Developing Your Influence from Anywhere in the Organization

In this first episode of Adventures in Machine Learning the panel talks about what machine learning is and what the podcast is about. Machine learning is rapidly becoming the future for many industries like banking, telecommunications, advertisements, etc. Yet there are so many subjects involved with it that novices may find it intimidating to get into, let alone start with.

Sponsor * Machine Learning for Software Engineers by Educative.io * CacheFly

Panel * Charles Max Wood * Gant Laborde * Eric Chalmers

Links * We fired our top talent. Best decision we ever made * https://www.coursera.org/learn/machine-learning

Picks Eric Chalmers:

Rodney Brooks: Robots, AI, and other stuff

Gant Laborde:

Beginning Machine Learning with TensorFlow.js

Charles Max Wood:

The Dev Rev

Follow Adventures in Machine Learning on Twitter > @DevChatTV