If you view a stack trace and it looks similar to the following:
RestException Traceback (most recent call last)
File <command-XXXXXXXXXXXX>:7
2 mlflow.sklearn.autolog()
...
File /databricks/python/lib/python3.9/site-packages/mlflow/tracking/fluent.py:349, in start_run(run_id, experiment_id, run_name, nested, tags, description)
345 user_specified_tags[MLFLOW_RUN_NAME] = run_name
347 resolved_tags = context_registry.resolve_tags(user_specified_tags)
--> 349 active_run_obj = client.create_run(
350 experiment_id=exp_id_for_run, tags=resolved_tags, run_name=run_name
351 )
353 _active_run_stack.append(ActiveRun(active_run_obj))
354 return _active_run_stack[-1]
You should validate that the service principal has the necessary edit permissions.
By granting edit permissions to the service principal, the job using the client.create_run method (the client is an instance of MlflowClient) can run successfully.
This edit permission ensures the necessary resources, such as the node with the specified ID, are accessible and available for the MLflow operation.
