Headless agents are coming for your data. Be ready with lakeFS
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
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
Scaling AI isn’t about building better models; it’s about building the system around them. Without consistency in data, workflows and governance, teams hit the same walls: A Center of Excellence (CoE) for Enterprise AI solves this by standardizing how AI is built, validated, and deployed – so teams can move faster without losing control. But
Building infrastructure for AI-ready data is a technical challenge, that’s for sure. But it’s also a strategic imperative for organizations looking to scale AI across the board. As datasets grow and models become more complex, teams will look for platforms that can consistently store, analyze, manage, and disseminate data throughout the AI lifecycle. Without a
Modern data systems are subject to constant change – new data arrives, pipelines evolve, and tables get updated many times a day. Without the ability to check the state of data at any point in the past, teams are forced to work in the dark when troubleshooting issues. On top of that, reproducing results becomes
As AI adoption evolves and teams advance from scattered ML trial projects to running AI as a production system, they inevitably face the question of how to operate such a system reliably at scale. This is where an AI Center of Excellence (AI CoE) comes in. It’s an organizational and technical response to that shift:
Modern data operations call for more than just lightning-fast queries and scalable storage. Safety, reproducibility, and control are all key parts of the equation. As Iceberg becomes the foundation for analytical and AI-driven applications, how you handle changes to your tables will determine whether your data platform is resilient or unstable. Iceberg branching is a
You probably heard the saying “Garbage in, garbage out.” It holds true for any data system but properly prepared and handled data is especially critical to the success of AI deployments. A study from IBM Institute for Business Value (IBV) showed that just 16% of AI programs are reaching enterprise level. How can teams create,
Based on my presentation at PyData Global 2025 When we – engineers – hear the word “compliance,” we tend to roll our eyes. We want to build features, not fill out forms. But here’s good news: the exact same tools that help you debug your code can also keep you out of trouble. In this
Preparing data for AI projects is about more than fast storage or shiny new table formats – it all starts with selecting the right data catalog to anchor your entire ecosystem. The catalog you pick specifies how your tables are discovered, versioned, secured, and evolved, which, in turn, impacts the reliability and clarity of every
2025 was a defining year for lakeFS. Across open source and Enterprise editions, we shipped major capabilities that expanded lakeFS from a powerful data versioning layer into a control plane for AI-Ready Data – spanning structured and unstructured data, multiple public and private clouds, and a growing ecosystem of analytics and ML engines. Here’s our
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