lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
We are excited to share that lakeFS is named in the “Coolest Vendor Innovations in Data Management” report from Gartner, published on July 29. The report states “In 2026, there is tremendous interest in deploying AI agents to automate data engineering tasks and operational and analytical workflows. Both vendors and end-user companies are investing heavily […]
As organizations incorporate multimodal data like images, video, music, documents, sensor data, and embeddings into increasingly complex AI systems, traditional data management techniques quickly become insufficient. Suddenly, teams are dealing with different data formats, interconnected pipelines, rising storage costs, and the issue of ensuring consistency and reproducibility. This article guides you through the fundamental ideas
A Frozen Model Is Not a Frozen System Talk to enough robotics teams and you hear the same line: “We’re using off-the-shelf models, so tracking what trained them isn’t really our problem.” It sounds reasonable. Someone else pre-trained the backbone. Your team didn’t touch the weights. Why would you need to reproduce training data you
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
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
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