Accelerating ML with Kubeflow for Autonomous Vehicles

Accelerating ML with Kubeflow for Autonomous Vehicles

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Accelerating ML with Kubeflow for Autonomous Vehicles

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  1. 1 [] Introduction to Ankit Aggarwal, Vinay Anantharaman, and Maurizio Vitale
  2. 2 [] Team Aurora's Introduction
  3. 3 [] Agenda
  4. 4 [] Introduction
  5. 5 [] Aurora Innovation is hiring!
  6. 6 [] AV Development Workflow
  7. 7 [] Pain points
  8. 8 [] Designing ML Orchestration Layer
  9. 9 [] Kubeflow Overview
  10. 10 [] Kubeflow Pipelines
  11. 11 [] Kubeflow Components Overview
  12. 12 [] Batch API
  13. 13 [] Sagemaker
  14. 14 [] GitHub PR Comments
  15. 15 [] Slack Notifier
  16. 16 [] Pipeline design
  17. 17 [] Developer Workflow
  18. 18 [] End-to-end Pipeline
  19. 19 [] Unified UI
  20. 20 [] Benefits
  21. 21 [] Kubeflow Infrastructure
  22. 22 [] Local Deployment
  23. 23 [] Multi-User
  24. 24 [] RDS
  25. 25 [] IAM
  26. 26 [] Vault
  27. 27 [] S3
  28. 28 [] User Autehntication
  29. 29 [] Groups
  30. 30 [] High Availability
  31. 31 [] Lessons Learned
  32. 32 [] Pipeline Runs
  33. 33 [] AVG Runs Per User
  34. 34 [] Pull Requests Verified
  35. 35 [] Back-end storage Opt-out
  36. 36 [] Other main competitors
  37. 37 [] Happiness on Kubeflow
  38. 38 [] TFX Tensorflow Serving
  39. 39 [] Track database access back to an individual user differentiating between individual users
  40. 40 [] Open-source Kubeflow
  41. 41 [] Open-source Community aligning values
  42. 42 [] Sagemaker components of Kubeflow
  43. 43 [] Kubeflow pipeline syntax
  44. 44 [] Recommendations to Starters
  45. 45 [] Vertex as a managed service
  46. 46 [] Deploying service to teams across the organization
  47. 47 [] Wrap up

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