Question

A data scientist runs a Databricks AutoML classification experiment. After it completes, they want to improve on the best model by manually adjusting a preprocessing step and re-tuning some hyperparameters, while keeping AutoML's automatically generated feature engineering as a starting point. What is the recommended way to do this within the AutoML workflow?

Accepted Answer

Open the generated source-code notebook for the best trial, clone or edit it, and modify the preprocessing and hyperparameters directly since AutoML uses a glass-box approach

Answer

Re-run the AutoML experiment with a longer timeout, because AutoML will eventually discover the manual changes on its own

Answer

Export the best model as a black-box artifact from the Model Registry and retrain it externally, since AutoML notebooks are read-only and cannot be modified

Answer

Delete the AutoML experiment and rebuild the entire pipeline from scratch in a new notebook without any AutoML assistance