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