UC Model Deployment across data bricks instances

[srkam]

Hello, We have multiple data bricks instances each represents an environment dev,qa,rel,prod etc.. We developed a  model in the dev workspace and registered in the UC model registry using mlflow. Now, we are trying to find a best way to deploy this registered model into the target environments. We want to avoid rerun of the training pipeline in the target env, instead promote/copy/transition the registered model, its version and experiments into the target env.

Can you please help us on how to achieve this?

Thanks

[iyashk-DB]

You can use UC's centralized model registry and MLflow’s copy APIs.

If all target workspaces attach to the same Unity Catalog metastore, reference and promote models via their 3‑level UC names; use MLflow’s copy_model_version to “copy” the exact artifacts from dev to qa/rel/prod, and manage deployment with aliases like Champion/Shadow. This avoids retraining and keeps one source of truth.
Ref Doc - https://docs.databricks.com/aws/en/machine-learning/manage-model-lifecycle

If environments run on different, isolated metastores/workspaces, use the community mlflow-export-import tooling to migrate registered models, versions, and experiments/runs between workspaces. This is the recommended way to copy MLflow objects (models, runs, experiments) across workspaces when UC sharing isn’t possible.
Ref Doc - https://github.com/mlflow/mlflow-export-import