Bringing ML Workflows to Heterogeneous Cloud Native Machine Learning Platforms Using Intermediate Representation

Bringing ML Workflows to Heterogeneous Cloud Native Machine Learning Platforms Using Intermediate Representation

Linux Foundation via YouTube Direct link

Abstraction Layer Features: Execution Client

13 of 14

13 of 14

Abstraction Layer Features: Execution Client

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Bringing ML Workflows to Heterogeneous Cloud Native Machine Learning Platforms Using Intermediate Representation

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  1. 1 Intro
  2. 2 Pillars of Al Lifecycle - Datasets, Models...
  3. 3 Define Pipeline with Python SDK
  4. 4 Kubeflow Pipelines with Tekton hits v1.0
  5. 5 Benefits of metadata and artifact tracking
  6. 6 Kubeflow Pipelines v2 main goals
  7. 7 Machine Learning Metadata in v1
  8. 8 Pipeline Spec in v1
  9. 9 Intermediate Representation in v2
  10. 10 Smart Runtime
  11. 11 Abstraction Layer for Orchestration Engines
  12. 12 Abstraction Layer Benefits
  13. 13 Abstraction Layer Features: Execution Client
  14. 14 Abstraction Layer Features: Execution Spec

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