lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
Introducing the lakeFS Summer Release: Governance for AI-Ready Data
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 […]
As AI moves into production, organizations are under growing pressure to govern the data behind it. Models are trained on petabytes of multimodal data. AI agents are modifying enterprise systems on their own. And new regulations (like the EU AI Act) are raising expectations around accountability, reproducibility, and access control. Yet for many teams, proving
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.
Running an ML workflow means you need a way to quickly access massive datasets. Downloading data from cloud storage before each experiment comes with clear limitations: it’s sluggish, expensive, and difficult to scale. Is there a way out? One approach ML teams are testing is mounting cloud storage as a local drive. Doing that reduces
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
Traditional RAG often feels like a black box. Vector search is powerful, but hard to reason about, and that makes AI agent knowledge versioning nearly impossible: you can’t track which version of your data an agent was served, who changed it, or why. Emerging standards such as Open Knowledge Format (OKF) aim to change that.
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 this post we build something most agent demos skip: an agent that does real work on real data, inside guardrails it can’t escape. The agent turns a messy folder of receipts and invoices into a clean, validated ledger, and it does it on a lakeFS branch mounted as an ordinary filesystem inside an E2B
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,
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