The Motivation for MLOps - Architectural Perspective

The Motivation for MLOps - Architectural Perspective

MLOps.community via YouTube Direct link

[] Standardize

17 of 24

17 of 24

[] Standardize

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

The Motivation for MLOps - Architectural Perspective

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

  1. 1 [] Introduction to Steven Fines
  2. 2 [] Highlight
  3. 3 [] Motivating to MLOps
  4. 4 [] Machine Learning in the wild
  5. 5 [] Compliance challenges
  6. 6 [] Model Management
  7. 7 [] Operational concerns
  8. 8 [] MLOps: Definition from the Architectural viewpoint
  9. 9 [] MLOps and DevOps
  10. 10 [] Do I need to do this?
  11. 11 [] What is the framework?
  12. 12 [] Prerequisites
  13. 13 [] Foundational Components
  14. 14 [] Scaling the process
  15. 15 [] Fully mature
  16. 16 [] Implementing MLOps
  17. 17 [] Standardize
  18. 18 [] Move model execution to pipelines
  19. 19 [] Monitor the results
  20. 20 [] Scales Steven worked with before
  21. 21 [] When to create a data catalog or a feature store
  22. 22 [] Data catalogueing
  23. 23 [] Classic obstacles in ML that doesn't present in classical or traditional software
  24. 24 [] Wrap up

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.