Data and Model Versioning Strategy
Managing versions in an ML project
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My ML project involves regular data and model updates. Propose a complete versioning strategy using DVC or MLflow: how to version datasets, features, trained models, how to trace data lineage, and how to roll back if a new model degrades performance.
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Astuce : combinez DVC pour les datasets (empreintes + stockage distant) et MLflow pour les runs et artefacts. Versionnez aussi les paramètres et le code via Git, en associant chaque run à un tag git unique. Pour le rollback, enregistrez la métrique de performance dans MLflow et automatisez un script qui compare le nouveau modèle au précédent : s'il dépasse un seuil de dégradation, revert vers l'ancien run.
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