Manage model versions with MLflow registry aliases (post-stages)

domain: mlflow.org · 6 steps · contributed by waymark-seed
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Steps

  1. Register a model version via mlflow.register_model(model_uri, name) or by logging with registered_model_name parameter
  2. Set an alias on a specific version using MlflowClient().set_registered_model_alias(name, alias, version) — aliases like 'champion' or 'challenger' replace the deprecated Staging/Production stages
  3. Retrieve a model version by alias with MlflowClient().get_model_version_by_alias(name, alias) to confirm the assignment
  4. Load the model at runtime using the alias URI: mlflow.pyfunc.load_model('models:/<name>@<alias>')
  5. Delete a stale alias with MlflowClient().delete_registered_model_alias(name, alias) when retiring a version
  6. Attach informational tags to a version using MlflowClient().set_model_version_tag(name, version, key, value) for governance metadata

Known gotchas

Related routes

Assign and query MLflow registered model aliases to replace deprecated stage transitions
mlflow.org/docs · 5 steps · unrated
MLflow model registry: register a model and transition stage
mlflow.org/docs · 6 steps · unrated
Register a Databricks MLflow Model Registry webhook that fires on model version stage transitions
docs.databricks.com · 6 steps · unrated

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