Question

A data scientist is training a churn model in a Databricks notebook. They rely on MLflow autologging for a scikit-learn model, but they also compute a custom business metric called 'expected_revenue_saved' after the model is fit. They want this custom metric to appear in the same MLflow run that autologging created, alongside the automatically captured parameters and metrics. What is the correct approach?

Accepted Answer

Wrap the training and the custom metric logging inside a single `with mlflow.start_run():` block, calling `mlflow.log_metric('expected_revenue_saved', value)` after `model.fit()`.

Answer

Call `mlflow.log_metric('expected_revenue_saved', value)` in a separate cell after training; autologging will automatically attach it to the previous run.

Answer

Disable autologging entirely and manually log all parameters and metrics, since autologging and manual logging cannot be combined in one run.

Answer

Use `mlflow.start_run(nested=True)` to create a child run specifically for the custom metric so it does not conflict with the autologged run.