Revision

1. ML system in production

2. ML System Fundamentals

3. Data System Fundamentals

4. Training Data

5. Class Unbalanced

6. Data Augmentation

7. Feature Engineering

8. Feature Evaluation

9. Data Leakage

10. ML Selection

11. Distributed Training

12. Offline Evaluation

13. Deployment

14. ML system failures

15. Continual Learning

16. ML System Design Steps