MLflow model registry: register a model and transition stage

domain: mlflow.org/docs · 6 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗

Steps

  1. Ensure your MLflow tracking server has a model registry backend (a database URI such as a SQL connection, not plain file store).
  2. 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.
  3. Retrieve the registered model version object returned by register_model to confirm the version number assigned.
  4. 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'.
  5. Add a description or tags to the version with client.update_model_version() for traceability.
  6. Load the production model in inference code using mlflow.pyfunc.load_model('models:/MODEL_NAME/Production').

Known gotchas

Related routes

Register a Databricks MLflow Model Registry webhook that fires on model version stage transitions
docs.databricks.com · 6 steps · unrated
Manage model versions with MLflow registry aliases (post-stages)
mlflow.org · 6 steps · unrated
Assign and query MLflow registered model aliases to replace deprecated stage transitions
mlflow.org/docs · 5 steps · unrated

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