Governance✓ Mathematical
◆ The PatternCentral catalog with staging, production, and archive states
A model registry is a versioned catalog where every model has metadata (who trained it, on what data, with which hyperparameters) and a lifecycle stage: staging → production → archived. Approval workflows ensure no model reaches production without review.
// Interactive — model lifecycle states
# Python — MLflow model registry import mlflow # Register a new model version result = mlflow.register_model( "runs:/abc123/model", "fraud-detector" ) # Promote to production client = mlflow.tracking.MlflowClient() client.transition_model_version_stage( name="fraud-detector", version=result.version, stage="Production" )
Pattern bridge: Model registries version models the way lineage tracking versions data — both create audit trails. In markets, strategy journaling serves the same purpose: versioned records of what you deployed and why.