Systems Engineering in Machine Learning - Navigating Low-Level Challenges

Systems Engineering in Machine Learning - Navigating Low-Level Challenges

MLOps.community via YouTube Direct link

[] Abstractions making sense now

17 of 23

17 of 23

[] Abstractions making sense now

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Systems Engineering in Machine Learning - Navigating Low-Level Challenges

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  1. 1 [] Andrew's preferred coffee
  2. 2 [] Introduction to Andrew Dye
  3. 3 [] Takeaways
  4. 4 [] Huge shoutout to our sponsors UnionML and UnionAI!
  5. 5 [] Andrew's background
  6. 6 [] Andrew's learning curve
  7. 7 [] Bridging the gap between firmware space and MLOps
  8. 8 [] In connection with Pytorch team
  9. 9 [] Things that should have learned sooner
  10. 10 [] Type of scale Andrew works on
  11. 11 [] Distributed training at Meta
  12. 12 [] Managing the huge search space
  13. 13 [] Execution patterns programs
  14. 14 [] Non-ML engineers dealing with ML engineers having the same skill set
  15. 15 [] Pace rapid change adoptation
  16. 16 [] Consensus challenges
  17. 17 [] Abstractions making sense now
  18. 18 [] Comparing to others
  19. 19 [] General principles in UnionAI tooling
  20. 20 [] Seeing the future
  21. 21 [] Inter-task checkpointing
  22. 22 [] Combining functionality with use cases
  23. 23 [] Wrap up

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