The lakeFS Blog
Filter by
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
- Yahli Ramberg
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,
- Idan Novogroder
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
- John Noonan
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
- Oz Katz
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
- Oz Katz
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
- Alexandria Yip, Iddo Avneri
In highly regulated environments, improving developer experience often comes at the cost of tighter controls. For companies handling sensitive personal data, even small workflow changes
- Gottfried Sehringer
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
- Oz Katz
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
