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ML Model Versioning Basics

Track models like code

Versioning ML models means storing model artifacts with linked code commits, data snapshots, parameters, and metrics so you can reproduce, compare, and roll back deployments reliably.

Track Artifacts

Store models with code hash, data version, params, and metrics.

Compare

Log experiments; keep lineage between training and serving versions.

Deploy Safely

Promote only validated versions; keep rollback-ready artifacts.

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