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