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Scaling Data and ML with Apache Spark and Feast - Feature Engineering for Production
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- 1 Intro
- 2 Machine learning at Gojek
- 3 Machine learning life cycle prior to Feast
- 4 Problems with end-to-end ML systems
- 5 Feast background
- 6 Machine learning life cycle with Feast
- 7 What is Feast?
- 8 What is Feast not?
- 9 Create entities and features using feature sets
- 10 Ingesting a DataFrame into Feast
- 11 Ingesting streams into Feast
- 12 What happens to the data?
- 13 Feature references and retrieval
- 14 Events throughout time
- 15 Ensuring point-in-time correctness
- 16 Point-in-time joins
- 17 Getting features for model training
- 18 Getting features during online serving
- 19 Feature validation in Feast
- 20 Infer TFDV schemas for features
- 21 Visualize and validate training dataset
- 22 What value does Feast unlock?
- 23 Roadmap
