Question

A data scientist is training a scikit-learn RandomForestClassifier inside a Databricks notebook. Before fitting the model, they call mlflow.sklearn.autolog(). They then wrap the training code in an explicit `with mlflow.start_run() as run:` block and call model.fit(). After the cell completes, what will MLflow have logged automatically to that run?

Accepted Answer

The model's hyperparameters, training metrics, and the fitted model artifact, all captured to the active run without additional logging calls

Answer

Only the model artifact; parameters and metrics must still be logged manually with mlflow.log_param and mlflow.log_metric

Answer

Nothing, because autolog() only works when start_run() is not called explicitly

Answer

Only the hyperparameters, since autologging cannot compute metrics without an explicit evaluation dataset