Manage ML model lifecycle with model registries. Covers MLflow Model Registry, model versioning (semantic, hash), model artifact storage, model metadata and tagging, model lifecycle stages (staging, production, archived), model promotion workflows, model lineage tracking (data to predictions), model packaging formats (ONNX, TorchScript, SavedModel, joblib), model signatures and schemas, model governance and approval, model comparison, rollback strategies, CI/CD integration for deployment, and model cards. Use when registering, versioning, promoting, or managing model artifacts.
