Scaling Data and ML with Apache Spark and Feast - Feature Engineering for Production

Scaling Data and ML with Apache Spark and Feast - Feature Engineering for Production

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Visualize and validate training dataset

21 of 23

21 of 23

Visualize and validate training dataset

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Scaling Data and ML with Apache Spark and Feast - Feature Engineering for Production

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

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