lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
In 1869, chemistry was a growing field, with a growing problem: 63 known elements and no organizing system that worked. Dmitri Mendeleev solved it by inventing the periodic table, which brought order to a field that had outgrown anyone’s ability to track it. Agentic AI is at its 1869 moment, though our challenge is even […]
When agents become the primary consumers of data, organizations need a secure, reproducible, and governed way to manage how those agents reach it. This article covers how AI agents access enterprise data in practice: the four access models, the core components behind them, the risks that show up in production, and why reproducibility decides whether
In highly regulated environments, improving developer experience often comes at the cost of tighter controls. For companies handling sensitive personal data, even small workflow changes can introduce compliance risks that are difficult to detect and even harder to fix at scale. The tension between usability and governance is especially visible in machine learning pipelines. Data
The now-infamous “pizza with glue” AI result is a symptom of something deeper than one bizarre edge case. When AI systems fail, the root cause is rarely mysterious. More often than not, bad outputs can be traced back to bad inputs: flawed data, unclear lineage, or uncontrolled environments. Smarter models won’t fix this on their
AI raises a question most organizations haven’t answered yet: who really controls the foundation? In a recent presentation at the AI-Ready Data Summit, Matthew Miller, Sr. Principal Chief Architect, Field CTO Office at Red Hat, showed that AI sovereignty isn’t a policy debate but an infrastructure strategy. Every AI system depends on choices about data,
Databricks built its reputation on openness. Spark. Delta Lake. MLflow. A company that rose by betting on open ecosystems over proprietary silos. Which is why Unity Catalog feels like such a sharp turn. And just now, the Pattern Got Harder to Ignore. This Week at Databricks’ Data + AI Summit, the Pattern Got Harder to
First presented at the AI-Ready Data Summit, this talk tackled the part of AI-ready data that tooling alone can’t solve: getting busy people to actually adopt it. AI-ready data is often framed as a technology challenge, but that framing misses the point. The real barrier often isn’t the tooling; it’s whether ML practitioners actually change
For the past few years, the hard work in AI has gone into models. Organizations spent that time learning, experimenting, and building the best models they could. That work paid off, and it cleared the way for what’s happening now, everywhere, at breakneck speed: agents. Companies have found real uses for agents across the organization,
AI is moving fast in life sciences. GxP is not. The teams that close that gap first get treatments to market faster. Pharma, biotech, and medical device teams are racing to put AI to work. Drug discovery is being accelerated. Clinical trial analytics are being modernized. Quality control on the manufacturing line is being automated.
AI-ready data is often misunderstood, dismissed as just another layer of hype on top of familiar practices like data quality. But that assumption misses something important. There is a real shift happening in how data needs to be prepared, structured, and managed to support modern AI systems. AI-ready data is critical to the success of
Most organizations today are experimenting with AI, but few have built the systems needed to make AI repeatable, scalable, and genuinely useful in production. That’s where Lockheed Martin stands apart. In a recent presentation at the AI-Ready Data Summit, Thomas Vander Wal shared how Lockheed Martin built what they call an AI Factory. Not a
The lakeFS Control Plane for AI-ready Data provides agents that rely on large, multimodal datasets, isolated access, verifiable results and built-in governance. TL;DR A new kind of consumer for your data A few weeks ago at TrailblazerDX 2026, Salesforce put a name on something the rest of the industry had been circling for months: Headless
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