Question

A data scientist is training a scikit-learn RandomForestClassifier inside a Databricks notebook. They want MLflow to automatically capture the model's hyperparameters, training metrics, and the fitted model artifact without adding explicit mlflow.log_param or mlflow.log_metric calls around their existing training code. What is the minimal change they should make before calling model.fit()?

Accepted Answer

Call mlflow.sklearn.autolog() before the fit call

Answer

Wrap the fit call in a with mlflow.start_run() block and manually call mlflow.sklearn.log_model()

Answer

Set mlflow.set_experiment() and call mlflow.log_artifacts() after fitting

Answer

Register the model in Unity Catalog with mlflow.register_model() before training