End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps
TL;DR
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.
Nauti's Take
End-to-end ML lineage is critical and often overlooked – DVC and SageMaker MLflow together make it concrete and reproducible. Teams shipping models without lineage tracking risk serious gaps during audits or incident investigations.
If you run ML in AWS, this two-pattern approach is worth evaluating for your stack.
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