Paolo Di Francesco

Paolo Di Francesco

Researcher, SDE, Senior Solutions Architect

Track your ML experiments end to end with Data Version Control and Amazon SageMaker Experiments

less than 1 minute read

Data scientists often work towards understanding the effects of various data preprocessing and feature engineering strategies in combination with different model architectures and hyperparameters. Doing so requires you to cover large parameter spaces iteratively, and it can be overwhelming to keep track of previously run configurations and results while keeping experiments reproducible.

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This post walks through how to use Data Version Control (DVC) alongside Amazon SageMaker Experiments to track data and code changes together with ML experiment metrics, providing end-to-end traceability for your ML workflows.

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