lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
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 […]
Addressing Compliance Requirements at Entrust Why Entrust Moved to lakeFS Tracking PII Access and Storage How lakeFS Improved Validation at Commit Time Indexing and Metadata After Merge The PII Lifecycle Inside the System Soft Deletion on Protected Branches Hard Deletion with Garbage Collection Unexpected Benefits: Versioning and Stable Training Inputs Using Tags and Diffs for
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,
Data agents are fast becoming the operating layer of enterprise AI – automating analysis, managing workflows, obtaining context, and acting across production systems. Headless agents are coming for your data, there’s no doubt about it. But while agent skills are improving at a breakneck pace, trust is still the biggest barrier to adoption. Denodo’s AI
Mounting object storage as a filesystem is the fastest way to get a notebook or Spark job reading S3, Azure Data Lake Storage, or GCS without rewriting it around an SDK. It is also the fastest way to discover what object storage does not give you: no atomicity across files, thin audit trails, and configuration
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.
As AI systems scale, data bottlenecks for AI projects quickly become one of the key barriers to model development and deployment. Slow pipelines, inconsistent datasets, and poor reproducibility show up as delayed testing, rising AI infrastructure costs, and lower model reliability. To support faster iteration and production-ready MLOps operations, teams need efficient data management at
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
Unless you’ve been living under a rock, you’ve probably heard of multimodal data and its integration, now a standard feature of modern data platforms. As systems ingest data ranging from structured tables to unstructured text, graphics, and streams, the difficulty shifts from data collection to data integration. What differentiates experimental pipelines from production-grade systems is
AI projects often end up failing due to data, not models. Inconsistent inputs, poor data quality, a lack of lineage, and fragmented workflows subtly weaken even the most sophisticated algorithms. As datasets grow in size and complexity, data management for AI projects evolves from a supporting role to a core engineering discipline. Without a solid
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