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Machine Learning Metadata in v1
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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 and Pipelines
- 4 Define Pipeline with Python SDK
- 5 Argo Workflows
- 6 Kubeflow Pipelines with Tekton hits v1.0
- 7 Benefits of metadata and artifact tracking
- 8 Lineage Tracking
- 9 TensorFlow Extended-Using MLMD as metadata store
- 10 Kubeflow Pipelines v2 main goals
- 11 Machine Learning Metadata in v1
- 12 Pipeline Spec in v1
- 13 Intermediate Representation in v2
- 14 New Orchestration Controllers
- 15 Components
- 16 Abstraction Layer for Orchestration Engines
- 17 Abstraction Layer Benefits
- 18 Abstraction Layer Features: Execution Client
- 19 Abstraction Layer Features: Execution Spec
- 20 Summary
- 21 References
- 22 Smart Runtime
