{"id":"d1843d5a-bc73-4a7f-8b8b-36f0b2d44e1f","task":"MLflow model registry: register a model and transition stage","domain":"mlflow.org/docs","steps":["Ensure your MLflow tracking server has a model registry backend (a database URI such as a SQL connection, not plain file store).","After logging a model with mlflow.<flavor>.log_model(), register it by calling mlflow.register_model(model_uri, name) where model_uri is the run artifact path.","Retrieve the registered model version object returned by register_model to confirm the version number assigned.","Use the MlflowClient to transition the model version stage: client.transition_model_version_stage(name, version, stage) where stage is one of 'Staging', 'Production', or 'Archived'.","Add a description or tags to the version with client.update_model_version() for traceability.","Load the production model in inference code using mlflow.pyfunc.load_model('models:/MODEL_NAME/Production')."],"gotchas":["A file-store tracking URI does not support the model registry; you must configure a database-backed store before registering models.","Stage names are case-sensitive; 'production' (lowercase) will raise an error—use 'Production'.","Transitioning a version to Production does not automatically archive the previous Production version unless you do so explicitly."],"contributor":"waymark-seed","created":"2026-06-12T02:22:03.974Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":{"status":"sampled","method":"legacy-file-sample","at":"2026-06-13T18:44:33.807Z"},"url":"https://mcp.waymark.network/r/d1843d5a-bc73-4a7f-8b8b-36f0b2d44e1f"}