Testing ML Models in Production - Detecting Data and Concept Drift

Testing ML Models in Production - Detecting Data and Concept Drift

Databricks via YouTube Direct link

Notebooks

10 of 35

10 of 35

Notebooks

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Testing ML Models in Production - Detecting Data and Concept Drift

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Intro
  2. 2 ML Cycle
  3. 3 Data Monitoring
  4. 4 KS Test
  5. 5 Categorical Features
  6. 6 Oneway Chisquared
  7. 7 Monitoring Tests
  8. 8 Tools
  9. 9 ML Flow
  10. 10 Notebooks
  11. 11 ML Workflow
  12. 12 ML Flow Delta
  13. 13 Other Notebooks
  14. 14 Widgets
  15. 15 Notebook Setup
  16. 16 Train Cycle Learn Pipeline
  17. 17 Data Logging
  18. 18 ML Flow Run
  19. 19 ML Flow Model Registry
  20. 20 ML Flow Experiment
  21. 21 Model Registry
  22. 22 New Data
  23. 23 Feature Checks
  24. 24 ML Flow Registry
  25. 25 Null Proportion
  26. 26 New Incoming Data
  27. 27 Chisquared Test
  28. 28 Action
  29. 29 Model Parameters
  30. 30 Model Staging
  31. 31 Model Migration
  32. 32 Missingness Check
  33. 33 Price Check
  34. 34 Categorical Check
  35. 35 Recap

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.