WEBINAR: Agents are already in your data. Find out how to stay in control.
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
The AI agent revolution is here. Coding agents like Claude Code, Cursor, and Codex are writing production software. Infrastructure agents are provisioning cloud resources. Data agents are transforming pipelines and updating dashboards. The trajectory is clear: agents will do more, not less. But there’s a pattern hiding in plain sight, one that explains why coding
Free Virtual Event for Enterprise AI Leaders Building AI that works in production is hard. For most enterprise teams, the biggest obstacle isn’t the model, it’s the data behind it. Study after study shows that organizations abandon AI projects due to poor data quality and inadequate data infrastructure. The gap between AI ambition and AI
This post recaps a comprehensive tutorial published by Alex Merced from Dremio and Tal Sofer from lakeFS, highlighting how version control transforms multimodal data management for AI teams. The Challenge: Keeping Diverse Data Types in Sync and Queriable Modern AI pipelines consume more than just structured data. Training sets include images, model artifacts, logs, and
From Inspiration to Action When we were still dreaming up lakeFS, one of the projects that inspired us was DVC (Data Version Control). It was one of those moments when you realize – “Ah, others see it too.” We weren’t alone in believing that data should be managed like code. DVC was built by data
We use cookies to improve your experience and understand how our site is used.
Join our community of experts:
introduce yourself, share your knowledge and discover best practices from fellow peers
