What does Databricks AutoML automatically produce after completing an experiment?
Select an answer to reveal the explanation.
Short Explanation and Infographic
AutoML is the rare tool that shows its work — like a tutor who not only gives you the answer but hands you the notebook so you can learn from and modify each step.
Full explanation below image
Full Explanation
Databricks AutoML runs multiple trials across different algorithms and preprocessing strategies, and for each trial it generates: a Python notebook with the full training code (so you can inspect and modify it), an MLflow run with logged parameters, metrics, and the model artifact. After the experiment completes, AutoML surfaces the best trial's notebook as the 'best model notebook' and registers the best model in MLflow. This transparency is a key differentiator — the generated notebooks are production-quality and editable, not black boxes. AutoML does not automatically deploy a REST API or create Feature Store tables. The user can optionally register the best model to Unity Catalog manually or proceed to build custom pipelines from the generated code.