Yuval Gorchover
I lead the AI/ML Engineering team at Voyantis, translating between data scientists, engineers, and business folks. My philosophy? Find the simplest solution that actually delivers.
I write about this stuff for Towards Data Science and practice yoga when I'm not debugging pipelines.
Session
Production ML failures often stem from one overlooked issue: features that work perfectly in a notebook break during inference. This is the "notebook chaos"—the silent killer of ML systems. This session dives deep into the root cause: the translation gap between research and production. We'll explore the architectural pattern of a feature store, demonstrating how to eliminate training-serving skew, ensure point-in-time correctness, and build reliable pipelines that turn notebook ideas into production gold.