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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,
- Iddo Avneri
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
- Oz Katz
Data agents are fast becoming the operating layer of enterprise AI – automating analysis, managing workflows, obtaining context, and acting across production systems. Headless agents
- Tal Sofer
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
- Joe Pringle
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
- Oz Katz
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
- Gottfried Sehringer
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
- Vince Antinozzi
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,
- Idan Novogroder
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
- Gottfried Sehringer
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
- Oz Katz
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
- Tal Sofer
