import os
import mlflow

if __name__ == "__main__":
    # attempt to use mlflow to search model registry and load model
    mlflow_client = mlflow.MlflowClient(
        registry_uri=os.getenv('MLFLOW_TRACKING_URI')
    )

    model_name = 'test-model'
    for model in mlflow_client.search_model_versions(filter_string=f"name='{model_name}'"):
        pymodel = mlflow.pyfunc.load_model(model_uri=f"models:/{model.name}/{model.version}")

If DATABRICKS_CLIENT_ID and DATABRICKS_CLIENT_SECRET were replaced by a personal access token at  DATABRICKS_TOKEN, then this works as expected. But not when using the Service principal's client id and secret, which is what I hope to use, so that auth is M2M.

Can you help here?