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Building AI that works once is relatively easy. Building AI you can trust every time is a discipline. That’s the central lesson CNH – one
- Gottfried Sehringer
As AI moves into production, organizations are under growing pressure to govern the data behind it. Models are trained on petabytes of multimodal data. AI
- John Noonan
Data has long been described as the foundation of AI. That part is not new. What is changing is the role data plays. In a
- Iddo Avneri
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
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
- 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
