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ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź

ML Platform Podcast

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MLOP Stack Extension - What Is This Extension?

The VC could be used for this, but just storing them in S3 and pointing in your experiment tracker to this location is good enough. So I would need to version data sets with feature store. And where will these store those? There are multiple ways as well. It solves a lot of problems. We have experiment tracker. What about pipelining frameworks? How does it work? Like, let's say airflow and feature store feature. These are two completely different worlds.

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