lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
Building infrastructure for AI-ready data is a technical challenge, that’s for sure. But it’s also a strategic imperative for organizations looking to scale AI across the board. As datasets grow and models become more complex, teams will look for platforms that can consistently store, analyze, manage, and disseminate data throughout the AI lifecycle. Without a […]
Modern data systems are subject to constant change – new data arrives, pipelines evolve, and tables get updated many times a day. Without the ability to check the state of data at any point in the past, teams are forced to work in the dark when troubleshooting issues. On top of that, reproducing results becomes
As AI adoption evolves and teams advance from scattered ML trial projects to running AI as a production system, they inevitably face the question of how to operate such a system reliably at scale. This is where an AI Center of Excellence (AI CoE) comes in. It’s an organizational and technical response to that shift:
Modern data operations call for more than just lightning-fast queries and scalable storage. Safety, reproducibility, and control are all key parts of the equation. As Iceberg becomes the foundation for analytical and AI-driven applications, how you handle changes to your tables will determine whether your data platform is resilient or unstable. Iceberg branching is a
You probably heard the saying “Garbage in, garbage out.” It holds true for any data system but properly prepared and handled data is especially critical to the success of AI deployments. A study from IBM Institute for Business Value (IBV) showed that just 16% of AI programs are reaching enterprise level. How can teams create,
The AI agent revolution is here. Coding agents like Claude Code, Cursor, and Codex are writing production software. Infrastructure agents are provisioning cloud resources. Data agents are transforming pipelines and updating dashboards. The trajectory is clear: agents will do more, not less. But there’s a pattern hiding in plain sight, one that explains why coding
AI systems increasingly influence decisions made in financial services, healthcare, and public services. This means teams need to be able to demonstrate that their models are transparent, fair, secure, and reproducible. Compliance is no longer just about policies and documentation – it requires technical guarantees embedded directly into data pipelines and ML workflows. Modern AI
Based on my presentation at PyData Global 2025. My colleague Yoav recently wrote about why reproducibility matters so much in healthcare AI and how data version control addresses the gap. This post is a follow up to that, with an overview of how to incorporate data version control into your existing ML workflow to address
As data quantities increase and pipelines become more complex, understanding where data originated, how it changed, and how it is used becomes increasingly important. This is why modern data systems generate more than just data – they also produce data denoting context: metadata. If teams don’t track metadata, they risk losing context that makes their
Based on my presentation at PyData Global 2025 When we – engineers – hear the word “compliance,” we tend to roll our eyes. We want to build features, not fill out forms. But here’s good news: the exact same tools that help you debug your code can also keep you out of trouble. In this
Over the last few years, AI and machine learning have moved from research projects into the core of the medical industry. Models now influence diagnosis, treatment planning, surgical systems, and patient outcomes. That shift changes the cost of uncertainty. This year, regulators won’t just evaluate what an AI or ML model does. They will increasingly
Preparing data for AI projects is about more than fast storage or shiny new table formats – it all starts with selecting the right data catalog to anchor your entire ecosystem. The catalog you pick specifies how your tables are discovered, versioned, secured, and evolved, which, in turn, impacts the reliability and clarity of every
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