AWS ML Blog
End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps
β’1 min readβ’
#deployment#compute
Level:Intermediate
For:ML Engineers, Data Scientists, AI Product Managers
β¦TL;DR
This article demonstrates how to integrate DVC, Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to establish end-to-end machine learning model lineage, enabling transparent and reproducible model development. By leveraging these tools, data scientists and engineers can track dataset and record-level changes, ensuring accountability and reliability in their ML workflows.
β‘ Key Takeaways
- DVC provides data version control, allowing for tracking of dataset changes and updates.
- Amazon SageMaker AI and MLflow Apps enable seamless integration and deployment of ML models, while also supporting end-to-end lineage.
- The proposed approach supports two deployable patterns: dataset-level lineage and record-level lineage, which can be implemented in an AWS account.
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