End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps
Amazon AWS AI Blog / 4/22/2026
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Key Points
- The article demonstrates an end-to-end approach to ML model lineage by combining DVC (Data Version Control), Amazon SageMaker AI, and SageMaker AI MLflow Apps.
- It presents two deployable patterns for lineage tracking: dataset-level lineage and record-level lineage.
- Readers can follow companion notebooks to run the patterns in their own AWS accounts, enabling practical lineage implementations.
- The focus is on improving traceability across the ML lifecycle, from data versions to deployed models via integrated tooling.
In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage. We walk through two deployable patterns — dataset-level lineage and record-level lineage — that you can run in your own AWS account using the companion notebooks.
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