From Mistakes to Success: Evolution of the ML Feature Store at Flocktory
summary
Describes Flocktory’s journey building an in-house ML Feature Store, moving from standard backend storage to a custom system optimized for Data Science and Machine Learning workflows. Covers the problem of inadequate generic storage for features and the practical engineering steps taken to design and implement a Feature Store tailored to their projects.
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chapters (7)
- 0:00Introduction
- 5:00Legacy feature extraction problems
- 10:17ML feature stores
- 16:48Offline feature store
- 22:11Online feature store optimization
- 28:21Results and plans
- 35:15Q&A
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