Model Versioning
Tracking and managing different iterations of ML models with their associated code, data, and configurations.
Overview
Model versioning is the practice of systematically tracking different iterations of machine learning models along with all artifacts needed to reproduce them — training code, data versions, hyperparameters, environment configurations, and evaluation results. It enables rollback, comparison, and audit trails.
Key Details
Effective model versioning goes beyond just saving model weights. Tools like DVC (Data Version Control), MLflow, and cloud ML platforms track the full lineage: which data, code, and parameters produced each model version. This is critical for debugging production issues, regulatory compliance, and collaborative ML development.