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Feathr: LinkedIn's High-performance Feature Store // David Stein // Coffee Sessions #120
MLOps.community
Lessons Learned from Spearheading the Creation of Feature Stores
The importance of having clean concepts that resonate with users is crucial when creating a feature store. It helps machine learning engineers think more simply about the system and the platform they need to deploy their models into. Getting these concepts clear early on reduces the need for course correction later. Mistakes and complex concepts make the cost of fixing problems increase over time. Working together with users to figure out how to specify feature dependencies and definitions is crucial. Using a thought device like configuration or DSL in the design process contributes to success and longevity.
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