lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
Introducing the lakeFS Summer Release: Governance for AI-Ready Data
Branching your database is the start. Branch your whole data ecosystem. Databricks published a good post recently about Lakebase, walking through how Glaspoort, a fiber operator in the Netherlands, ships database changes with the same discipline they ship application code. Every environment branches from production. Every pull request gets its own fresh, disposable database. The […]
Building AI that works once is relatively easy. Building AI you can trust every time is a discipline. That’s the central lesson CNH – one of the leading manufacturers of agricultural and construction equipment – learned as its machine learning team grew into an engineering organization operating at the sharp edge of safety-critical systems. The
Data has long been described as the foundation of AI. That part is not new. What is changing is the role data plays. In a recent presentation at the AI-Ready Data Summit, Himabindu Tummala, Distinguished Engineer at Dell Technologies, showed how AI is evolving right now and what that evolution demands from the data layer.
Traditional data architectures are starting to show their limitations as organizations move beyond analytics and into production with generative AI and autonomous apps. Fragmented data, pipelines riddled with issues, and ineffective governance make it impossible for teams to replicate or trust AI outputs. Organizations need to provide data teams with a solid architecture to produce
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,
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