Orchestrate all the Things: Recent Episodes
George Anadiotis
Connecting the dots with George Anadiotis: Analyst, Consultant, Engineer, Founder, Host, Researcher, and Writer.
Stories about Tech, Data, AI and Media, and how they flow into each other shaping our lives.
I’ve engaged from the likes of Gary Marcus and Andrew Ng to emerging thinkers and innovators across multiple domains.
My stories have been featured on ZDNet and VentureBeat, and are syndicated across DZone, Hackernoon, Medium and Substack.
Some might call this futurism; let’s just say it’s connecting the dots
Many conversations have a technical focus. Most also examine business perspectives and use cases, while others are socio-technical.
Some are analyses on emerging themes – picking them up early, featuring expert comment, or offering alternative takes.
Others cover breaking news, typically also featuring the people behind them plus some analysis. There are some book reviews as well.
I focus on the connection between data, analytics, data science, graphs, machine learning and AI and their impact on society and business.
I have been covering topics related to:
- AI and Machine Learning
- Data, Analytics and Data Science
- Knowledge Graphs, Graph Databases, Graph AI & Data Science
- Innovation, and a wide array of technologies such as Blockchain, Cloud, Observability, IoT, Open Data and Open Source, Social Media and Software Engineering.
For inquiries, please use https://linkeddataorchestration.com/contact/
Neo4j recently announced new product features in collaboration with Google, as well as a new Chief Product Officer coming from Google: Sudhir Hasbe.
We caught up to discuss what the future holds for Neo4j as well as the broader graph database space.
Article published on Orchestrate all the Things.
In an era of dried-up funding and Data Lakehouse vendor supremacy, Redpanda is going against the grain.
The company just secured a $100 million Series C funding round to execute on an unconventional strategy.
Redpanda Founder and CEO Alex Gallego explains how things work for the company.
Article published on Orchestrate all the Things
The EU Parliament just voted to bring the EU AI Act regulation into effect. If GDPR is anything to go by, that's a big deal.
Here's what and how it's likely to effect, its blind spots, what happens next, and how you can prepare for it based on what we know.
Article published on Orchestrate all the Things
Can technology, and real-time technology in particular, help companies achieve savings during economic hardship? Alok Pareek thinks it can.
Pareek is the Co-founder and EVP of products of Striim, a vendor whose goal and motto is to "help companies make data useful the instant it’s born".
Depending on which angle you look at it, you could say that Pareek is either biased or in the know. Either way, it was not so long ago that real-time data, or streaming data as this market is also called, was estimated to be worth billions.
But then again, as the recent wave of layoffs and market capitalization losses goes to show, many projections around technology are off the mark.
Could real-time data be different? Where does cloud modernization come into play and how does Striim's offering relate to that? As Striim today announced the availability of its fully managed Striim Cloud service on Amazon Web Services (AWS), we connected with Pareek to discuss.
Retail is big business. But like many other sectors it's undergoing a transformation, largely affected by the shift of consumer behavior from physical to digital. Many retailers are looking to analytics and AI to help them cope with the challenges.
Andrew Ng, among the most prominent figures in AI, is now turning his sights to doing precisely that with his new venture Netail
Founded in 2022 as part of Landing AI, Netail, a technology that enables retailers to auto-identify competitors across the internet and track their assortments, availability and optimize prices in real-time, today announced the closing of $5M in seed funding.
We connected with retail veteran Mark Chrystal who is Netail's CEO to discuss the changing landscape in retail and Netail's offering.
Quantum computing could be a disruptive technology. It's founded on exotic-sounding physics and it bears the promise of solving certain classes of problems with unprecedented speed and efficiency. The problem, however, is that to this day there's been too much promise and not enough delivery.
D-Wave is the company that pioneered quantum computing. In this exclusive interview, D-Wave CEO Alan Baratz talks about about quantum computing fundamentals and how this is related to the market’s current state, real-world clients and use cases, and what the future holds for this space.
Graph database Neo4j has just released version 5, which promises better ease of use and performance through improvements in its query language and engine, as well as automated scale-out and convergence across deployments.
We caught up with Jim Webber, Chief Scientist at Neo4j, to discuss Neo4j 5 as well as the bigger picture in the graph market.
Article published on VentureBeat
AIOps is what you get when you combine big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination. At least, that's how Gartner defines AIOps.
Based on this definition, as well as coverage of vendors that have products they label with the AIOps moniker, you'd be inclined to think that AIOps is mostly about anomaly detection and remediation. But what about provisioning, configuration, deployment and orchestration?
These are all essential parts of IT operations which have not received as much AIOps attention. They also happen to be at the core of Ansible, Red Hat's open source IT automation tool.
Now Red Hat is embarking on a new direction for Ansible with Project Wisdom, aiming to take automation to the next level in collaboration with IBM Research. Red Hat refers to Project Wisdom as the first community project to create an intelligent, natural language processing capability for Ansible and the IT automation industry. We connected with Red Hat Vice President & General Manager for the Ansible Business Unit Tom Anderson to discuss Project Wisdom's premises, status and trajectory.
Shining light on AI research and applications in a conversation with the curators of the most comprehensive report on all things AI.
If you think AI is moving at a breakneck speed and it's almost impossible to keep up, you are not alone. Even if being on top of all things AI is part of your job, it's getting increasingly hard to do that. Nathan Benaich and Ian Hogarth know this all too well, yet somehow they manage.
Benaich and Hogarth have solid backgrounds in AI as well as tons of experience and involvement in research, community- and market-driven initiatives. AI is their both their job and their passion and being on top of all things AI comes with the territory.
Nathan Benaich is the General Partner of Air Street Capital, a venture capital firm investing in Al-first technology and life science companies. lan Hogarth is a co-founder at Plural, an investment platform for experienced founders to help the most ambitious European startups.
Since 2018 Benaich and Hogarth have been publishing their yearly State of AI report, aiming to summarize and share their knowledge with the world. This ever-growing and evolving work covers all the latest and greatest across industry, research and politics. Over time, new sections are added, with this year featuring AI Safety for the first time.
Traditionally, Benaich and Hogarth have also been venturing on predictions, with remarkable success. Equally traditionally, we have been connecting with them to discuss their findings every year upon release of the report. This year was no exception, so buckle up and let the ride begin.
Article published on Linked Data Orchestration.
Economic theory is known to be constrained by a number of inefficiencies in its modeling. Salesforce researchers claim AI can help address that, leading to more robust economic policies.
Article published on ZDNet
As Ben Lorica will readily admit, at the risk of dating himself, he belongs to the first generation of data scientists. In addition to having served as Chief Data Scientist for the likes of Databricks and O'Reilly, Lorica advises and works with a number of venture capitals, startups and enterprises, conducts surveys, and chairs some of the top data and AI events in the world. That gives him a unique vantage point to identify developments in this space.
Having worked in academia teaching applied mathematics and statistics for years, at some point Lorica realized that he wanted his work to have more practical implications. At that point the term "data science" was not yet coined, and Lorica's exit strategy was to become a quant. Fast forwarding to today, Lorica still has friends in the venture capital world.
That includes Intel Capital's Assaf Araki, with whom Lorica co-authored two recent posts on data management and AI trends. We caught up with Lorica to discuss those, as well as new areas for growth and the trouble with unicorns and what to do about it.
What's a good enough weather prediction? That's a question most people probably don't give much thought to, as the answer seems obvious -- an accurate one. But then again, most people are not CTOs at DTN.
Lars Ewe is, and his answer may be different than most people's. With 180 meteorologists on staff providing weather predictions worldwide, DTN is the largest weather company you've probably never heard of.
Weather forecast, too, is all about data and models these days. But balancing accuracy and viability is a fine act, especially at global scale and when the stakes are high.
Article published on ZDNet
Everything counts in large amounts. You don't have to be Google, or to build large AI models, to benefit from writing efficient code. But how do you measure that?
It's complicated, but that does not mean that people are not trying. That's what Abhishek Gupta and the Green Software Foundation (GSF) are relentlessly working on.
What does sustainability actually mean for organizations? Can it be measured, and if yes, how? Obvious questions with less than obvious answers, even for sustainability and ESG professionals like James Phare.
Phare shares his experience and assessment of sustainability and its relationship with the ESG space, its current state and trajectory, and how data and analytics can help.
Award-winning AI research - check. Startup and enterprise experience - check. Venture capital and Mark Benioff backing - check. Is that enough for Richard Socher's you.com to take on Google?
Here is why and how he aims to do that.
Decision intelligence is one of those terms that sound vaguely familiar, even if you've never come across it before. Like many category-defining terms, it can mean different things to different people. This is a feature category-defining terms either have by design, or acquire through extensive use.
In October 2021, Gartner identified DI as a 2022 Top Trend. A number of vendors have identified with that category, and Aera Technology is among them, claiming to have been doing DI before it was called DI.
Today, Aera is announcing new capabilities for its Aera Decision Cloud at the Gartner Supply Chain Symposium/Xpo™ 2022. Aera founder and CTO Shariq Mansoor weighed in on DI, Aera's offering, and how it is relevant for supply chains and beyond.
It sounds like a contradiction in terms, but disaster and disruption management is a thing. Disaster and disruption is precisely what ensues when catastrophic natural events occur, and unfortunately, the trajectory the world is on seems to be exacerbating the issue. In 2021 alone, the US experienced 15+ weather/climate disaster events with damages exceeding $1 billion.
Previously, we have explored various aspects of the ways data science and machine learning intertwine with natural events - from weather prediction, to the impact of climate change on extreme phenomena and measuring the impact of disaster relief. AiDash, however, is aiming at something different: helping utility and energy companies as well as governments and cities manage the impact of natural disasters, including storms and wildfires.
We connected with AiDash co-founder and CEO Abhishek Singh to learn more about its mission and approach, as well its newly released Disaster and Disruption Management System (DDMS)
Is deep learning really going to be able to do everything? Opinions on the potential of this opinion to prove true vary. Geoffrey Hinton, awarded for pioneering deep learning, is not entirely unbiased in so opining. However others, including Hinton's deep learning collaborator Yoshua Bengio, are looking to infuse deep learning with elements of a domain still under the radar: operations research.
Machine learning and its deep learning variety are practically household names by now. There is lots of hype around deep learning, as well as a growing number of applications. However, as applications of deep learning are proliferating, its limitations are also becoming better understood. Presumably that's the reason why Bengio turned his attention to operations research.
In 2020, Bengio and his collaborators surveyed recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. They advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so.
To this day, however, there is no publicly visible operations research renaissance to speak of, and commercial applications remain few compared to machine learning. Nikolaj van Omme and Funartech want to change that.
Article published on VentureBeat
Data quality is part of data intelligence. It's a topic that lot of people are concerned about, and it makes engagement and adoption around data intelligence solutions better. With many data quality solutions with different approaches available in the market, customers need to be able to choose the one that works best for them.
Plus, if you are someone like Alation, a vendor whose core business is not data quality -- if you can't beat them, join them. As Alation CEO and co-founder Satyen Sangani shared, that was the thinking behind today's announcement of the Alation Open Data Quality Initiative (ODQI) for the modern data stack.
As Alation notes, the program provides customers with the freedom of choice and flexibility when choosing the best data quality and data observability vendors to fit the needs of their modern, data-driven organizations. We caught up with Sangani to discuss the ODQI and where it fits in the broader data intelligence landscape, as well as Alation's strategy and evolution.
Article published on VentureBeat
Circa 2017, there was a lot of hype around autonomous driving. If one were to take that at face value, it would mean that by now autonomous driving would have been a reality already. Apparently that's not the case, and Alex Kendall claims to have known that all along. Still, that did not stop him from setting out then, and he's still working on it today.
Kendall is the co-founder and CEO of Wayve, a company founded in 2017 to tackle the challenge of autonomous driving based on a deep learning approach. Today, Wayve announced a partnership with Microsoft to leverage the supercomputing infrastructure needed to support the development of AI-based models for autonomous vehicles on a global scale.
We caught up with Kendall to discuss Wayve's philosophy and approach, its current status, as well as where the partnership with Microsoft fits in. Spoiler alert: this is not your typical "commercial application provider partners with cloud vendor" story.
Article published on VentureBeat
There is an implicit assumption in most analytics solutions: the data being analyzed, and the insights derived, are almost exclusively quantitative. That is, they refer to numerical data, such as number of customers, sales, and the like.
But when it comes to customer feedback, perhaps the most important data is qualitative: text contained in sources such as feedback forms and surveys, tickets, chat and email messages. The problem with that data is that, while valuable, they require domain experts and a lot of time to read through and classify. Or at least, that was the case up until now.
This is the problem Viable is looking to address.
After data privacy and GDPR, the EU wants to leave its mark on AI by regulating it with the EU AI Act.
Here's what it is, what it means for the world at large, when it's expected to take effect, how it will work in practice, as well as Mozilla's recommendations for improving it, and ways for everyone be involved in the process.
Article published on ZDNet
What do you get when you combine two of the most up-and-coming paradigms in data processing - streaming and graphs? A potential game-changer, and this is the bet first DARPA and now CrowdStrike Falcon Fund have taken on thatDot and its open source framework Quine.
The CrowdStrike Falcon Fund is an investment vehicle managed by CrowdStrike, in partnership with Accel, that makes cross-stage private investments within cybersecurity and adjacent markets.
DARPA is also known to have an interest in cybersecurity, and this is what motivated the decision to fund the development of a framework recently released by thatDot as an open source project dubbed Quine.
Many solutions exist on the market both for streaming data processing as well as for graph analytics, oftentimes working in tandem. However, thatDot Founder and CEO Ryan Wright claims that Quine's technology is unique, enabling it scale to orders of magnitude beyond what any other system today is capable of.
We caught up with Wright to discuss the key premises behind Quine and thatDot, as well as the practical aspects of using Quine and the next steps in its evolution.
Article published on VentureBeat
Amazon just unveiled Serverless Inference, a new option for SageMaker, its fully managed machine learning (ML) service. The goal for Amazon SageMaker Serverless Inference is to serve use cases with intermittent or infrequent traffic patterns, lowering total cost of ownership (TCO) and making the service easier to use.
We connected with Bratin Saha, AWS VP of Machine Learning, to discuss where Amazon SageMaker Serverless fits into the big picture of Amazon’s machine learning offering and how it affects ease of use and TCO, as well as Amazon’s philosophy and process in developing its machine learning portfolio.
Article published on VentureBeat
Modern agriculture was broken long before pandemics, wars, supply chain disruptions and fertilizer shortages. Regenerative agriculture can fix that, and data can help.
What could a multi-national corporation like Unilever and an environmental leader and activist like Vandana Shiva possibly have in common?
Shiva has a history of actively opposing the commodification and appropriation of natural resources for the benefit of corporate interests. Unilever is at the heart of the international corporate web.
Shiva, a prolific author, just published her latest book: "Agroecology and Regenerative Agriculture: Sustainable Solutions for Hunger, Poverty, and Climate Change".
Unilever, whose products need around 4 million hectares of land to grow the raw materials for, recently published a new set of regenerative agriculture principles.
There has to be something about regenerative agriculture. Let's take a look at what it is and why it's important, what the data tells us about it, and how analytics and AI may help going forward.
Article published on ZDNet
Whether you're genuinely interested in getting insights and solving problems using data, or just attracted by what has been called “the most promising career” by LinkedIn and the “best job in America” by Glassdoor, chances are you're familiar with data science. But what about graph data science?
As we've elaborated previously, graphs are a universal data structure with manifestations that span a wide spectrum: from analytics to databases, and from knowledge management to data science, machine learning and even hardware.
Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points -- that's the the 30 second explanation according to Alicia Frame.
Frame is the Senior Director of Product Management for Data Science at Neo4j, a leading graph database vendor. She has a PhD in computational biology, and has spent ten years as a practicing data scientist working with connected data.
When she joined Neo4j about 3 years ago, she set out to build a best in class solution for dealing with connected data for data scientists.
Today, the product Frame is leading at Neo4j, aptly called Graph Data Science, is celebrating its two-year anniversary with version 2.0 which brings some important advancements: new features, a native Python client, and availability as a managed service under the name AuraDS on Google Cloud.
We caught up with Frame to discuss graph data science the concept, and Graph Data Science the product.
Processing data in real-time is on the rise. The streaming analytics market (which depending on definitions, may just be one segment of the streaming data market) is projected to grow from $15.4 billion in 2021 to $50.1 billion in 2026, at a Compound Annual Growth Rate (CAGR) of 26.5% during the forecast period as per Markets and Markets.
A multitude of streaming data alternatives, each with its own focus and approach, has emerged in the last few years. One of those alternatives is Apache Pulsar. In 2021, Pulsar ranked as a Top 5 Apache Software Foundation project and surpassed Apache Kafka in monthly active contributors.
In another episode in the data streaming saga, StreamNative just released a report comparing Apache Pulsar to Apache Kafka in terms of performance benchmarks.
We caught up with StreamNative Chief Architect & Head of Cloud Engineering Addison Higham to discuss the report's findings, as well as the bigger picture in data streaming.
Article published on VentureBeat
There's no business like powering business. That may seem like a paradox, and most of us don't really think of accounting as a separate line of business. In that sense, it's a secret hiding in plain sight: accounting is big business, in and of itself.
The global accounting services market is expected to reach $735.94 billion in 2025, and digital transformation has targeted it for impact. The size of the accounting software market is projected to reach $22,9 billion by 2027.
This includes names from Microsoft, Oracle and SAP to Intuit and Xero, plus many others. Yet, there's something none of these names are applying, says Isaac Heller: AI in accounting. That's why Heller and Amir Boldo co-founded Trullion in 2019.
Trullion seems to be on a mission to disrupt the accounting software market by "unifying the unstructured and structured worlds of accounting by reading PDF or Excel-based contracts and translating them into financial workflows, connected to the data source".
In February 2022, Trullion announced that it has closed $15 million in Series A funding. Trullion has more than 100 clients, a mixture of large enterprise clients and audit firms which procure directly for their clients, and has crossed the seven figure mark in recurring revenue.
We caught up with Heller to discuss Trullion's focus and its use of AI to innovate in the accounting software market.
Article published on VentureBeat
A pragmatic and direct approach to ethics and trust in artificial intelligence (AI) — who would not want that? This is how Beena Ammanath describes her new book "Trustworthy AI.”
Ammanath is the executive director of the Global Deloitte AI Institute and is well-qualified to write a book on trustworthy AI. She has had stints at GE, HPE and Bank of America in roles such as vice president of data science and innovation, and CTO of artificial intelligence as well as lead of data and analytics.
Ammanath said part of her motivation to write the book was her desire to help organizations start off with ethics and trust in AI on the right foot and another part was her frustration with existing approaches to AI Ethics.
Article published on VentureBeat
Arguably, two of the most iconic examples of digital transformation are Kodak and Amazon. Kodak failed to keep with the times, leading to the demise of a once dominant commercial empire. Amazon, however, had the foresight to not just “stay in its lane,” but step out and shape the world’s digital infrastructure with AWS.
Now, Lexmark, is taking note of Kodak’s failure and Amazon’s success and piloting into its own. Moving beyond printers, supplies and accessories — which is how Lexmark first made a name for itself — the company’s current motto seems to be “Print, secure and manage your information.”
In 2022, Lexmark lists Print and Capture as just two of its solutions, which also include Cloud, Security and IoT. The Lexmark Optra IoT Platform, unveiled September, is featured prominently on the company’s site.
Today, the company is unveiling Optra Edge, the latest addition to its Optra IoT solutions portfolio. Vishal Gupta, Lexmark’s senior VP of connected technology, CTO and CIO, says the company’s new tools and move into IoT are just the beginning.
Article published on VentureBeat
Did you ever feel you've had enough of your current line of work, and wanted to shift gears? If you have, you're definitely not alone.
Besides the Great Resignation, however, there are also less radical approaches, like the one Andrew Ng is taking.
Ng is among the most prominent figures in AI. Founder of deeplearning.ai, Co-Chairman and Co-Founder of Coursera, and Adjunct Professor at Stanford University. He was also Chief Scientist at Baidu Inc., and Founder & Lead for the Google Brain Project.
Yet, his current priority has shifted -- from bits to things, as he puts it.
Andrew Ng is also the Founder & CEO of Landing AI, a startup working on facilitating the adoption of AI in manufacturing since 2017. This effort has apparently contributed in shaping Ng's perception of what it takes to get AI to work beyond Big Tech, in what he calls the data-centric approach.
We connected with Ng to discuss the data-centric approach to AI, and how it relates to his work with Landing AI and the big picture of AI today.
Run:AI offers a virtualization layer for AI, aiming to facilitate AI infrastructure. It's seeing good traction, and just raised a $75M Series C funding round.
Here's how the evolution of the AI landscape has shaped its growth.
Article published on ZDNet
What if what you think you know about developer productivity and the value of software is off the mark, and that is hurting the quality of your software, the operation of your organization, as well as your bottomline?
Article published on ZDNet
Cloud-native is the name of the game for application development. The creators of the Fluent Bit and Fluentd popular open-source projects for cloud-native observability are launching an offering aimed at the enterprise.
Article published on ZDNet
Maintaining compatibility with the de-facto market standard, while reimplementing and extending it. It's easier said than done, but that's what Redpanda is doing, and it seems to be working.
Article published on ZDNet
Is there a line connecting machine learning observability to explainability, leading to responsible AI? Aporia, an observability platform for machine learning, thinks so.
Applications are proliferating, cloud complexity is exploding, and Kubernetes is prevailing as the foundation for application deployment in the cloud. That sounds like an optimization task ripe for machine learning, and StormForge is doing just that.
Article published on ZDNet
H2O is an open-source based AI powerhouse with a grand vision. The latest addition to its product line wants to bring the AI capabilities of the web giants to the rest of the world, says CEO and Founder Sri Ambati.
Article published on ZDNet
ScyllaDB started out as with the aim of becoming a drop-in replacement for Cassandra. It's growing to become more than that.
Article published on ZDNet
Training deep learning models is costly and hard, but not as much as deploying and running them in production. Deci wants to help address that..
Article published on ZDNet
As more organizations are turning to synthetic data to feed their data-hungry machine learning algorithms, Rendered.ai wants to help.
Article published on ZDNet
What do we talk about, when we talk about AI Ethics? Just like AI itself, definitions for AI ethics seem to abound.
From algorithmic and dataset bias, to the use of AI in asymmetrical / unlawful ways, to privacy and environmental impact.
All of that, and more, could potentially fall under the AI Ethics umbrella.
We try to navigate this domain, and lay out some concrete definitions and actions that could help move the domain forward
A new comprehensive report is out, using data science and analytics to slice and dice the crypto industry. Add to that insights from the report's authors, and you have the state of the crypto industry today.
Article published on ZDNet
MinIO took an early bet in the cloud, and in Amazon's S3 becoming the de facto standard for application storage needs. The bet is paying off.
Article published on ZDNet
In theory, Web3 is all about decentralization. In practice, it can be very centralized.
API3 is a bold effort to achieve decentralization in connecting Web3 applications to the outside world.
Article published on ZDNet
Would you trust AI that has been trained on synthetic data, as opposed to real world data? You may not know it, but you are probably already doing it, and it's fine, according to the findings of a newly released survey.
Piece published on VentureBeat.
OctoML is announcing the latest release of its platform to automate deployment of production-ready models across the broadest array of clouds, hardware devices and machine learning acceleration engines.
Article published on ZDNet
55.000 projects, 30.000 developers, $54M funding, and customers including the likes of NASA, in a bit over 2 years. Edge Impulse is riding the wave of machine learning at the edge.
Article published on ZDNet
RocksDB is the secret sauce underlying many data management systems. Speedb is a drop-in replacement for RocksDB that offers a significant boost in performance and now powers Redis on Flash.
Article published on ZDNet
SambaNova just added another offering under its umbrella of AI-as-a-service portfolio for enterprises: GPT language models.
As the company continues to execute on its vision, we caught up with CEO Rodrigo Liang to look both at the big picture and under the hood.
Article published on ZDNet
A number of successive funding rounds have given Yugabyte unicorn status, while positioning the company to aim for a big piece of a growing pie in the shape-shifting database market
Article published on ZDNet
Knowledge graphs are probably the best technology we have for data integration. But what about application integration? Knowledge graphs can help there, too, argues EnterpriseWeb
Article published on ZDNet
IOTA is unveiling its smart contracts, with a clear onboarding path and many interesting features for developers.
We discuss this release, as well as progress made since moving to the new network and other new features in the works with IOTA Foundation co-founder and CEO Dominik Schiener.
Article published on ZDNet
It's this time of year again: reports on the state of AI for 2021 are out.
In what is becoming a valued yearly tradition, we caught up with AI investors and authors of the State of AI report, Nathan Benaich and Ian Hogarth, to discuss the 2021 release.
Some of the topics we covered are lessons learned from operationalizing AI and MLOps, new concepts and datasets, language models, AI ethics, and AI-powered biotech and pharma.
Article published on ZDNet
Appwrite, an open source platform that offers a slew of features to developers, aims to capitalize on its grass-roots popularity.
Article published on ZDNet
A multi-trillion dollar business in crisis, upending incumbents, unfettered ambition, and pragmatic deep learning. FLYR's story has it all.
Article published on ZDNet
Being able to deploy machine learning applications at the edge bears the promise of unlocking a multi-billion dollar market.
For that to happen, hardware and software must work in tandem. Arm's partner ecosystem exemplifies this, with hardware and software vendors like Alif and Neuton working together.
Article published on ZDNet
Will Deep Learning really be able to do everything? We don't really know.
But if it's going to, it will have to assimilate how classical computer science algorithms work. This is what DeepMind is working on, and its success is important to the eventual uptake of neural networks in wider commercial applications.
This work goes by the name of Neural Algorithmic Reasoning. Join us as we discuss roots and first principles, the defining characteristics, similarities and differences of algorithms and Deep Learning models with the people who came up with this.
We also cover the details of how Neural Algorithmic Reasoning works, as well as future directions and applications in areas such as path finding for Google Maps
Could this be the one algorithm to rule them all?
Article published on VentureBeat.
Image: Getty
MLOps is the art and science of bringing machine learning to production, and it means many things to many people. The State of MLOps is an effort to define and monitor this market
Article published on ZDNet
GraphQL is a specification that came at just the right time to address an age-old issue in software engineering: service integration. Apollo's implementation is seeing lots of traction, and it just got more gas in the tank for its grand vision that goes well beyond integration.
Article published on ZDNet
Machine learning is eating the world, and spilling over to established disciplines in software, too. After MLOps, is the world ready to welcome MLGUI?
Philip Vollet is somewhat of a celebrity, all things considered. Miley Cyrus or Lebron James he is not, at least not yet, but if data science lives up to the hype, who knows.
As the senior data engineer with KPMG Germany, Vollet leads a small team of machine learning and data engineers building the integration layer for internal company data, with access standardization for internal and external stakeholders. Outside of KPMG, Vollet has built a tool chain to find, process, and share content on data science, machine learning, natural language processing, and open source using exactly those technologies, which makes for a case of meta, if nothing else.
There is a flood of social media influencers sharing perspectives on data science and machine learning. While most influencers direct their attention solely toward issues of model building and infrastructure scaling, Vollet also looks at the user view, or frameworks for building user interfaces for applications utilizing machine learning. We were intrigued to discuss with him how building these user interfaces is necessary to unlock AI's true potential.
Article published on VentureBeat.
Photo by Kelly Sikkema on Unsplash
Open source software used to be poorly understood by commercial forces, and it's often approached in a biased way. A new generation of investment funds goes to show that things are changing.
Open source software is a lot of things to a lot of people. For some people -- engineers, mostly -- it's a way to work on their passion, be involved in a community, and give something back to the world. For others -- business people, mostly -- it's a way to grow projects organically, and sell software without actually investing too much in sales.
It's a nuanced topic, and we've tried to explore it from many angles. From the business angle, exploring open source vendors relationships with hyperscalers, mostly Google and Amazon. From the contributor and license engineering angle, exploring different models for commercial open source projects. And from the community angle, exploring metrics for community health and value generation evaluation.
Today, we explore open source software (OSS) and its commercialization from yet another angle - the investment angle. There are a couple of venture capitals out there that seem to be ahead of the curve in terms of their understanding of, and investment in, commercial open source companies. Runa Capital is one of them, and we caught up with Konstantin Vinogradov, Runa Capital Principal, who shared views, findings, and outlook for commercial OSS.
Article published on ZDNet.
Image: Shutterstock
It's a data terminology mess out there. Let's try and untangle it, because there's more to words than lingo.
Hopefully technology investment decisions in your organization are made based on more than hype. But as technology is evolving faster than ever, it's hard to keep up with all the terminology that describes it. Some people see terminology as an obfuscation layer meant to glorify the ones who come up with it, hype products, and make people who throw terms around appear smart.
There may be some truth in this, but that does not mean terminology is useless. Terminology is there to address a real need, which is to describe emerging concepts in a fast moving domain. Ideally, a shared vocabulary should facilitate understanding of different concepts, market segments, and products.
Case in point - data and metadata management. Have you heard the terms data management, data observability, data fabric, data mesh, DataOps, MLOps and AIOps before? Do you know what each of them means, exactly, and how they are all related? Here's your chance to find out, getting definitions right from the source - seasoned experts working in the field.
Article published on ZDNet
A $325 million Series F funding round, bringing Neo4j's valuation to over $2 billion. A social network of 3 billion people, distributed across 1000 servers. The latter is a demo, the former is not. But both are real signs that the graph market and Neo4j are getting seriously big.
If you're into the market and investment side of things, how does a Series F funding round as part of a $325 million investment led by Eurazeo and GV (formerly Google Ventures), bringing Neo4j's valuation to over $2 billion sound? Pretty impressive, probably.
If you're into the technology and applications side of things, how does a Neo4j demo of a social network application with 3 billion people, running queries designed to test the limits of graph query languages and databases across a 1000 node cluster sound? Equally impressive, probably.
Graph database vendor Neo4j CEO and co-founder Emil Eifrem is announcing the former and showcasing the latter today, at the company's annual virtual conference NODES. We caught up with Eifrem to get a taste of things to come.
Article published on ZDNet
Being able to deploy machine learning applications at the edge is the key to unlocking a multi-billion dollar market. TinyML is the art and science of producing machine learning models frugal enough to work at the edge, and it's seeing rapid growth.
Edge computing is booming. Although the definition of what constitutes edge computing is a bit fuzzy, the idea is simple. It's about taking compute out of the data center, and bringing it as close to where the action is as possible.
Whether it's stand-alone IoT sensors, devices of all kinds, drones, or autonomous vehicles, there's one thing in common. Increasingly, data generated on the edge are used to feed applications powered by machine learning models.
There's just one problem: machine learning models were never designed to be deployed on the edge. Not until now, at least. Enter TinyML.
Tiny machine learning (TinyML) is broadly defined as a fast growing field of machine learning technologies and applications including hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices.
Article published on ZDNet
Graphs are everywhere. That has been the motto of graph afficionados for years, and now it seems that the world is waking up to this.
How would you feel if you saw demand for your favorite topic, which also happens to be your line of business, grow 1000% in two-years time? Vindicated, overjoyed, and a bit overstretched in trying to keep up with demand, probably.
Although Emil Eifrem never used those exact words when we discussed the past, present and future of graphs, that's a reasonable projection to make. Eifrem is the CEO and co-founder of Neo4j, a graph database company which lays claims to having popularized the term "graph database", and to leading the graph database category.
Eifrem and Neo4j's story and insights are interesting because through them we can trace what is shaping up as a foundational technology stack for the 2020s and beyond: graphs.
"Graph Relates Everything" is how Gartner put it, when including graphs in its top 10 data and analytics technology trends for 2021. Interest is expanding as graph data takes on a role in master data management, tracking laundered money, connecting Facebook friends and powering Google, in search and beyond.
Think Panama Papers researchers, NASA engineers, and Fortune 500 leaders: they all use graphs. Here's why, and how.
Article published on VentureBeat.
Image: Getty Images
Ensuring data quality is essential for analytics, data science and machine learning. Superconductive's Great Expectations open source framework wants to do for data quality what test-driven development did for software quality
Technical debt is a well-known concept in software development. It's what happens when unclear or forgotten assumptions are buried inside a complex, interconnected codebase, and it leads to poor software quality. The same thing also applies to data pipelines, it's called pipeline debt, and it's time we did something about it.
That's the gist of what motivated Abe Gong and James Campbell to start Great Expectations in 2018. Great Expectations is an open-source tool that aims to make it easier to test data pipelines, and therefore increase data quality.
Superconductive, the force behind Great Expectations, has announced it has received $21 million in Series A funding led by Index Ventures with CRV and Root Ventures participating. We caught up with Gong to learn more about Great Expectations.
Article published on ZDNet
Tuning databases is key to application performance and stability, but it's a hard job. Auto-tuning helps, but it was reserved for the Oracles and Microsofts of the world till now. OtterTune wants to democratize this capability
Databases are the substrate on which most applications run. Although different applications have different needs served by different databases, they all have one thing in common: they are complex systems that need continuous fine tuning to work optimally.
Databases come with a plethora of parameters that can be tuned by "turning knobs". Traditionally, this has been the job of Database Administrators (DBAs). Their job is a hard one, as they need to know the specifics of the database, the hardware it's running on, and the workloads it serves.
Some database vendors like IBM, Microsoft and Oracle have taken steps to automate this work. OtterTune is a startup that wants to democratize this capability. Today OtterTune is announcing the private beta of its new automatic database tuning service, as well as an initial $2.5 million seed funding round led by Accel.
Article published on ZDNet
NeuReality targets deep learning inference workloads on the edge, aiming to reduce CAPEX and OPEX for infrastructure owners
The AI chip space is booming, with innovation coming from a slew of startups in addition to the usual suspects. You may never have heard of NeuReality before, but it seems likely you'll be hearing more about it after today.
NeuReality is a startup founded in Israel in 2019. Today it has announced NR1-P, which it dubs a novel AI-centric inference platform. That's a bold claim for a previously unknown, and a very short time to arrive there -- even if it is the first of more implementations to follow.
Article published on ZDNet
Trying to capture the value open source software generates can be a bit chaotic. The CHAOSS project may lend a helping hand.
By now, the tension between commercial interests and open source projects is well known.
Trying to balance building a sustainable business and a community around open source software, in a cloud-first world is not the easiest thing in the world.
In the latest episodes of a long-winding saga, two more commercial open source vendors, Elastic and Grafana, changed their licenses.
Their rationale was clearly communicated as trying to protect the business they have built around the respective open source projects from cloud vendors that they feel compete unfairly with them, without contributing as much as they do.
Interestingly, all parties involved refer to "the community" as being front and center in what they do. While obviously important, however, what constitutes an open source community, how it's faring, and what value it generates all seem rather vaguely defined.
The people working on the CHAOSS project under the auspices of the Linux Foundation want to change that. We caught up with Georg J.P. Link, CHAOSS project co-founder, to find out more.
Article published on ZDNet
Open-source stacks enabled software to eat the world. Some of the most innovative companies in the world are working on building an open-source stack for AI.
Dan Jeffries was there when the LAMP stack enabled software to eat the world. Perhaps you don’t know, or remember, what the LAMP stack is, but it's actually pretty important.
LAMP is an acronym made out of the initials of key open-source technologies used in software development - Linux, Apache, MySQL, and PHP. These technologies were hotly debated back in the day. Today, they are so successful that the LAMP stack has become ubiquitous, invisible, and boring.
AI, on the other hand, is a hot topic today. Just like the LAMP stack turned software development into a commodity and made it a bit boring (especially if you're not a professional software engineer), an AI stack should turn AI into a commodity - and make it a bit boring, except maybe for data engineers. This is what Dan Jeffries is out to do with the AI Infrastructure Alliance (AIIA).
Article published on VentureBeat.
A new whitepaper just released by leading blockchain oracle service Chainlink lays the foundation for new capabilities for application and smart contract developers.
Chainlink provides an oracle service, enabling smart contracts to interoperate with the world. Today, Chainlink released a whitepaper outlining what they dub Chainlink 2.0. We connected with Chainlink co-founder Sergey Nazarov to discuss what this means.
Article published on ZDNet
Google uses machine learning and graphs to deliver search results. Most search engines do not. Weaviate wants to change that.
Bob van Luijt's career in technology started at age 15, building web sites to help people sell toothbrushes online. Not many 15 year-olds do that today, and fewer still did it then. Apparently that gave van Luijt enough of a head start to arrive at the confluence of technology trends today.
Van Luijt went on to study arts, but ended up working full time in technology anyway. In 2015, when Google introduced its RankBrain Algorithm, the quality of search results jumped up. It was a watershed moment, as it introduced machine learning in search.
A few people noticed, including van Luijt, who saw a business opportunity, and decided to bring this to the masses.
Article published on ZDNet
GraphQL was never conceived as a query language for databases. Yet, it's increasingly being used for this purpose. Here's why, and how.
Manish Jain and Josh McKenzie are both engineer rock stars who wear many hats. They also have something else in common: they are both avid GraphQL users and builders, despite getting there from different start points.
Article published on ZDNet
What do you get when you juxtapose two of the hottest domains today - AI and healthcare? A peek into the future, potentially.
In 2020, few things went well and saw growth. Artificial intelligence was one of them, and healthcare was another one. Artificial intelligence remained on a steady course of growth and further exploration -- perhaps because of the Covid-19 crisis. Healthcare was a big area for AI investment.
Today, the results of a new survey focusing precisely on the adoption of AI in healthcare are being unveiled. We caught up with 2 of its architects: Gradient Flow Principal Ben Lorica, and John Snow Labs CTO David Talby, to discuss findings and the state of AI in healthcare.
Article published on ZDNet.
In 2014, IOTA set out to offer an alternative to the key issues with blockchain: Scalability and transaction fees. Somewhere along the way, things went wrong. Not everything is lost, however, and IOTA is looking to regain momentum.
IOTA's main premise, namely solving the issues around blockchain by introducing a different data structure, remains. IOTA, like blockchains such as Bitcoin or Ethereum, is a distributed ledger. Unlike those, however, the data structure it uses is a directed acyclic graph, called the Tangle.
We discuss with IOTA Foundation Co-founder and CEO, Dominik Schiener, on what IOTA got wrong, what it got right, and what is being done to build on what it got right and fix what it got wrong.
Starting with the release of a new wallet, IOTA has been reinvented and rewritten from the ground up over the last one and a half years. This new phase of the project is called Chrysalis, introducing a network upgrade. It promises a truly decentralized solution with high throughput and no transaction fees, oracles, and smart contracts.
Article published on ZDNet
Machine learning operations, or MLOps, is the art and science of taking machine learning models from the data science lab to production.
It's been a hot topic for the last couple of years, and for good reason. Going from innovation to scalability and repeatability are the hallmarks of generating business value, and MLOps represents precisely that for machine learning.
Apache TVM has become a de facto standard in MLOps, and OctoML is the company gearing its commercialization and scale up.
As OctoML secured a $28 million Series B funding round, we caught up with its CEO and co-founder Luis Ceze to discuss TVM, OctoML, and MLOps.
Article published on ZDNet
Another day, another funding round in the graph market
Katana Graph, a high-performance scale-out graph processing, AI and analytics company, announced a $28.5 million Series A financing round led by Intel Capital.
We discuss with Keshav Pingali, Katana Graph CEO and co-founder, on the company's background, technology, and prospects
Article published on ZDNet
The largest funding round to date in the graph market is good news not just for TigerGraph, but for the market at large.
We review TigerGraph's progress and the market landscape with TigerGraph CEO Yu Xu and COO Todd Blaschka
Article published on ZDNet
As it turns out, the answer to the question of how to make the best of AI hardware may not be solely, or even primarily, related to hardware
Today's episode features Determined AI CEO and Founder, Evan Sparks. Sparks is a PhD veteran of Berkeley's AmpLab with a long track record of accurate predictions in the chip market.
We talk about an interoperability layer for disparate hardware stacks, ONNX and TVM -- two ways to solve similar problems, AI constraints and energy efficiency, and Infusing knowledge in models
Article published on ZDNet
Vectors are foundational for machine learning applications. Pinecone, a specialized cloud database for vectors, has secured significant investment from the people who brought Snowflake to the world. Could this be the next big thing?
Article published on ZDNet
AI chips, MLOps, and ethics. Knowledge, and Graphs. COVID-19 as a mixed bag for technological progress and adoption
Article published on ZDNet
Blockchain's DeFi-ning moment. Cloud, Kubernetes, and GraphQL. Open source is winning, open source creators are losing. A reality check on key technological drivers for the new decade.
Article published on ZDNet
Gary Marcus is one of the more prominent, and controversial, figures in AI
In this in-depth conversation, we cover everything from his background and early work in cognitive psychology as a way to understand the human mind, to his critique on Deep Learning, a holistic proposal for robust AI, the role of background knowledge, and knowledge graphs in particular, and the future of AI
Python, the most popular language for data science and machine learning, gets a huge boost from Dask, an open source framework for running it in a distributed way on top of GPUs.
Saturn Cloud, a startup offering Dask as service, is now a Snowflake partner, making Dask available to the masses.
We discuss all about Dask and Saturn Cloud with co-founder Sebastian Metti.
Article published on ZDNet
You might not know it by reading this news, but Blaize is an AI chip company. Blaize is now boldly going where none of its ilk has gone before, releasing a software development product. And that's not the only reason AI Studio is interesting.
We discuss with Blaize CEO Dinakar Munagala and VP R&D Dmitry Zakharchenko to explore AI Studio, its potential and philosophy, and where it fits in Blaize's strategy.
Article published on ZDNet
A flexible API is key to database accessibility and developer friendliness today. Apache Cassandra was lacking in that department, and DataStax is trying to address this with the release of a new API layer called Stargate.
A discussion with Ed Anuff, formerly of Apogee and Google Cloud, and currently DataStax Chief Product Officer, on the rationale behind Stargate, its architecture and operation, how it compares to GraphQL, and a roadmap for the future.
Article published on ZDNet
Fluree is a hitherto under the radar graph database that uses blockchain to support data lineage and verification.
Learn all about Fluree, its origins and milestones, use cases, data integration and integrity, open source, and more
Featuring Brian Platz, Fluree co-founder and co-CEO.
Article published on ZDNet
From fit for purpose development to pie in the sky research, this is what AI looks like in 2020.
A discussion on all things AI, with authors of the State of AI 2020 Report, Nathan Benaich and Ian Hogarth.
Benaich and Hogarth work on the intersection of industry, research, investment and policy, with extensive background and various currently held positions such as venture capital investor and researcher. This gives them a unique vantage point on all things AI.
Their report, which is published for the 3rd year in a row, is their way of sharing their insights with the AI ecosystem at large.
Article published on ZDNet
Beyond Limits is an industrial and enterprise-grade AI technology company active in energy, utilities, and healthcare, which just announced a milestone Series C funding round of $133 million, led by Group 42 and BP.
The company's approach to applications of AI in industrial settings originates from NASA Jet Propulsion Labs. It's a hybrid approach, combining data-driven machine learning, with knowledge-based symbolic AI, plus the elements of spatial and temporal cognition.
We discuss business, technology, applications, and the future of AI with CEO and Founder AJ Abdallat
Article published on ZDNet
Netdata, the company behind the eponymous open-source, distributed, real-time, performance, and health monitoring solution for systems and applications, has announced it completed a new round of financing of 14,2 million dollars.
We take the opportunity to dive in the Netdata story with CEO and Founder Costas Tsaousis: what they do and why, their open source approach and distributed architecture, and their position in the overall observability ecosystem.
Article published on ZDNet
Τhis episode is all about multi-model and graph databases.
A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.
ArangoDB CEO and co-founder Claudius Weinberger, and ArangoDB Head of Engineering and Machine Learning Jörg Schad discuss multi-model, machine learning, hype, and the database market in general.
Article published on ZDNet
The perfect observability storm with open source leading the way, and a partnership that makes sense.
Open source is eating the world, and observability is no exception. New Relic and Grafana Labs just announced a partnership, and we discuss the specifics as well as the broader open source and observability landscape with Grafana Labs CEO Raj Dutt and New Relic Chief Product Officer Bill Staples.
We cover everything from the rationale of the partnership, what it brings for users and how the integration was done, to open source, de facto and de jure standards for telemetry, and the use of AI and machine learning.
Article published on ZDNet
Did you ever wonder how data is used in the medical industry? The picture that emerges leaves a lot to be desired.
In the early 90s,the evidence-based medicine movement tried to make medicine more data-driven. Three decades later, we have more data, but not enough context, or transparency.
Today's episode features David Scales, Chief Medical Officer at Critica, and an Assistant Professor of Medicine at Weill Cornell Medical College. Scales specialized in internal medicine, and also has a PhD in sociology, with a particular interest in the sociology of science.
The conversation with David covers the fundamentals of evidence-based medicine, how data is generated through Randomized controlled trials and accessed via Cochrane Reviews.
We draw parallels with best practices in data science and data governance, touching upon provenance, context, and metadata.
We also explore the dark side of data in the medical industry: pharmaceutical company involvement, bias, the controversy around Cochrane, and the tyranny of the Randomized controlled trial. We also explore predictive models and data parasites in COVID-19 times, and the role of the World Health Organization
jSonar offers a security layer that sits on top of databases. Let's face it: database security is not the most appealing topic for most people. Bennatan is very aware of it.
That, however, has not stopped him and the jSonar team from carving out a spot for themselves. And it has not stopped Goldman Sachs from investing 50 million dollars in jSonar either.
Let's hear why, and what makes jSonar special.
Article published on ZDNet in June 2020
Data warehouses alone don't cut it. Data lakes alone don't cut it either. So whether you call it data lakehouse or by any other name, you need the best of both worlds, says Databricks.
A new query engine and a visualization layer are the next pieces in Databricks' puzzle. We connected with Ali Ghodsi, co-founder and CEO of Databricks, to discuss their latest news: the announcement of a new query engine called Delta Engine, and the acquisition of Redash, an open source visualization product.
Our discussion started with the background on data lakehouses, which is the term Databricks is advocating to signify the coalescing of data warehouses and data lakes.
We talked about trends such as multi cloud and machine learning that lead to a new reality, how data warehouses and data lakes work, and what does the data lakehouse bring to the table.
We also talked about Delta Engine and Redash of course, and we wrapped up with an outlook on Databricks business growth.
ZDNet article published in June 2020
The conflict between access to data and data sovereignty is key to understanding how AI works, and moving it forward.
The Ocean Protocol Foundation wants to help resolve that conflict, by introducing a way of letting AI work with data without giving up control.
The goal is to decentralize data science and AI using blockchain, and the latest milestone called Compute-to-Data brings this one step closer
We connected with Trent McConaghy, Ocean Protocol Foundation Founder, to discuss how it all works.
Article published on ZDNet in June 2020
Streamlit is an open source framework that wants to revolutionize building machine learning and data science applications, and just secured a $21 million Series A funding to try and do this.
Streamlit wants to be for data science what business intelligence tools have been for databases: a quick way to get results, without bothering much with the details.
In today's episode, we welcome Streamlit CEO and Founder, Adrien Treuille.
We discuss what makes Streamlit special, how it works, and where data-driven applications at large are going next.
Article published on ZDNet in June 2020.
Your good old on-premise SQL database is in terminal decline. A pure-play open-source cloud-native PostgreSQL, with support for Apache Cassandra and GraphQL interfaces, is what you need.
Or at least, this is what the Yugabyte crew thinks. The company, founded by Facebook data infrastructure veterans, announced that it has raised $30 million in an oversubscribed Series B round to double down on community and team growth. This is a crowded market, but big enough to be a non-zero-sum game.
We connected with Yugabyte founders Kannan Muthukkaruppan and Karthik Ranganathan, and newly recruited CEO Bill Cook, previously of Sun Microsystems and Pivotal, for a deep dive in the company, the funding, and the market.
Article published on ZDNet in June 2020
Fact or fiction? That's not always an easy question to answer. Incomplete knowledge, context and bias typically come into play. In the nascent domain of scientific fact checking, things are complicated.
If you think fact-checking is hard, which it is, then what would you say about verifying scientific claims, on COVID-19 no less? Hint: it's also hard -- different in some ways, similar in some others.
Fact or Fiction: Verifying Scientific Claims is the title of a research paper published on pre-print server Arxiv by a team of researchers from the Allen Institute for Artificial Intelligence (AI2), with data and code available on GitHub.
In this backstage chat, David Wadden, lead author of the paper and a visiting researcher at AI2 and George Anadiotis connected to discuss the rationale, details, and directions for this work.
Article published on ZDNet in May 2020
Now that the dust from Nvidia's unveiling of its new Ampere AI chip has settled, let's take a look at the AI chip market behind the scenes and away from the spotlight.
Few people, Nvidia's competitors included, would dispute the fact that Nvidia is calling the shots in the AI chip game today. The announcement of the new Ampere AI chip in Nvidia's main event, GTC, stole the spotlight.
Let's put the new architecture into perspective by comparing against the competition in terms of performance, economics, and software.
Article published on ZDNet in May 2020
From user, to partner and investor. That's not a very common scenario for software vendors, especially if the user-cum-partner-investor is someone like Visa.
GoodData is evolving more than its relationship with select users.
In this backstage chat, GoodData CEO and Founder Roman Stanek and George Anadiotis discuss the ins and outs of the deal, the data landscape, the way data are used to shape directions for organizations big and small, and what's next for GoodData.
Article published on ZDNet, May 2020
Grafana Labs, makers of popular open-source observability platform Grafana, announced the general availability of Grafana 7.0.
This comes only a few months after Grafana Labs scored $24 million in Series A funding to double down on open-source strategy and build what it dubs the world's first open and composable observability platform.
In this backstage chat, CEO and Founder Raj Dutt and George Anadiotis connected to discuss Grafana 7.0, and the road forward.
Article published on ZDNet, May 2020.
Knowledge graphs are among the most important technologies for the 2020s.
Here is how they are evolving, with vendors and standard bodies listening, and platforms becoming fluent in many query languages.
Article published on ZDNet, January 2020.