model registry

A model registry is a central repository for storing, versioning, and managing trained machine learning models. **Core features**: **Versioning**: Track model versions with metadata. **Storage**: Store model artifacts (weights, configs) reliably. **Lineage**: Record training data, code, parameters used. **Lifecycle**: Manage stages (development, staging, production). **Access control**: Permissions for teams and environments. **Benefits**: Reproducibility (recreate any model version), governance (track what is deployed), collaboration (team shares models), rollback capability. **Common registries**: MLflow Model Registry, Weights and Biases, Sagemaker Model Registry, Vertex AI Model Registry, custom solutions. **Metadata stored**: Model version, accuracy metrics, training config, data version, author, timestamp, stage. **Integration**: CI/CD pipelines pull from registry for deployment. Training pipelines push new versions. **Comparison shopping**: Compare versions on metrics before promoting. **Governance**: Approval workflows for production deployment. Audit trail for compliance. **Best practices**: Register all models (including experiments), include comprehensive metadata, automate promotion workflows.