It might be possible with a bit of code via mlflow client api ( there seems to be a way to run list_registered_models and extract info ) - but haven't tried it out. If the requirement is to share models between workspaces, one approach could be to have a central registry workspace as documented in https://docs.databricks.com/applications/machine-learning/manage-model-lifecycle/multiple-workspaces...