Assign and query MLflow registered model aliases to replace deprecated stage transitions

domain: mlflow.org/docs · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗

Steps

  1. Register a model version via mlflow.register_model() or the fluent API after a run
  2. Use MlflowClient().set_registered_model_alias(name, alias, version) to assign aliases like 'champion' or 'challenger'
  3. Retrieve the aliased version with client.get_model_version_by_alias(name, alias) for downstream consumers
  4. Load the model in serving code via models:/<name>@<alias> URI to decouple version pinning from deployment code
  5. Delete stale aliases with client.delete_registered_model_alias() when a version is retired

Known gotchas

Related routes

Manage model versions with MLflow registry aliases (post-stages)
mlflow.org · 6 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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