Question

A data scientist finished a training run tracked in MLflow. She now wants to programmatically download a preprocessing pipeline object (saved earlier with mlflow.log_artifact under the path 'preprocessing/pipeline.pkl') from that specific run so she can reuse it in a separate notebook. She has the run_id. Which approach correctly retrieves the artifact file to local storage?

Accepted Answer

Use mlflow.artifacts.download_artifacts(run_id=run_id, artifact_path='preprocessing/pipeline.pkl') to download the file locally

Answer

Call mlflow.get_run(run_id).data.artifacts['preprocessing/pipeline.pkl'] to read the object directly into memory

Answer

Use mlflow.pyfunc.load_model(f'runs:/{run_id}/preprocessing/pipeline.pkl') to load the pipeline

Answer

Query mlflow.search_runs() and access the returned DataFrame column 'artifacts.preprocessing.pipeline.pkl'