Large Language Models at Scale - Challenges and Opportunities

Large Language Models at Scale - Challenges and Opportunities

MLOps.community via YouTube Direct link

[] Time spent red teaming the models

33 of 34

33 of 34

[] Time spent red teaming the models

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Large Language Models at Scale - Challenges and Opportunities

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

  1. 1 [] Nils' preferred coffee
  2. 2 [] Nils' background
  3. 3 [] Takeaways
  4. 4 [] Subscribe to our Newsletters and IRL Meetups, and leave your reviews!
  5. 5 [] Nils' history
  6. 6 [] From IT Security to Machine Learning
  7. 7 [] Tangibility of IT and Security
  8. 8 [] NLP transition
  9. 9 [] Bad augmentation to new capabilities of LLMs
  10. 10 [] Nils' concern during his PH.D.
  11. 11 [] Making Money from Machine Learning
  12. 12 [] Catastrophic forgetting
  13. 13 [] Updating solutions
  14. 14 [] Neural search space and building adaptive models
  15. 15 [] Filtering models
  16. 16 [] Latency issues
  17. 17 [] Models running in parallel
  18. 18 [] Generative models problems
  19. 19 [] Nils' role at Cohere
  20. 20 [] To build or not to build API
  21. 21 [] Search models
  22. 22 [] Large use cases
  23. 23 [] Open source discussion within Cohere
  24. 24 [] Competitive Edge
  25. 25 [] Future world of API
  26. 26 [] LLMs in Production Conference Part 2 announcement!
  27. 27 [] Hopeful direction of Cohere's future
  28. 28 [] Data silos
  29. 29 [] Where to update the database and code
  30. 30 [] Nils' focus
  31. 31 [] Make money or save money
  32. 32 [] Cohere's upcoming project
  33. 33 [] Time spent red teaming the models
  34. 34 [] 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.