Question

A data scientist is starting a new project in a Databricks notebook and wants every scikit-learn model training call to be tracked automatically, including parameters, metrics, and the fitted model, without wrapping each training call in explicit logging code. They also want all runs to be recorded under a specific experiment they created in the workspace. Which combination of steps accomplishes this with the least manual logging effort?

Accepted Answer

Call mlflow.set_experiment() with the experiment path, then call mlflow.sklearn.autolog() before fitting the estimators.

Answer

Call mlflow.start_run() manually around each .fit() call and use mlflow.log_param() and mlflow.log_metric() for every value.

Answer

Set the experiment with mlflow.set_experiment(), then call mlflow.log_model() after each .fit() to capture parameters and metrics.

Answer

Call mlflow.autolog(disable=True) and register the model directly to Unity Catalog after training.