How to create fully automated ML workflows with Amazon SageMaker Pipelines
summary
Demonstration builds a full pipeline using the SageMaker Pipelines Python SDK that runs a scikit‑learn processing step, XGBoost training job, evaluation script and a conditional model registration into the Model Registry, with Git-triggered CICD via CodeCommit/CodeBuild/CodePipeline and an approval gate that promotes versions to staging and prod. The pipeline shows caching, lineage tracking, cross‑account model access, and parameterized instance types; a pushed code change automatically starts a new execution and successfully registers and deploys a model.
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chapters (6)
- 0:00Introduction to SageMaker Pipelines
- 3:27Challenges in ML workflows
- 13:20SageMaker Pipelines overview
- 17:59Building a model pipeline
- 23:12Model registry and deployment
- 28:22Summary and availability
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