Question

A data scientist is training an XGBoost model inside a Databricks notebook and wants MLflow to automatically capture hyperparameters and evaluation metrics for each run. However, because the models are very large and storage is a concern, they do NOT want the fitted model artifact itself to be logged automatically. Which approach accomplishes this?

Accepted Answer

Call mlflow.xgboost.autolog(log_models=False) before training so parameters and metrics are captured but the model artifact is not logged.

Answer

Call mlflow.autolog(disable=True) to prevent artifact bloat while still capturing parameters and metrics.

Answer

Set mlflow.xgboost.autolog(log_input_examples=False) so the model artifact is skipped while metrics are recorded.

Answer

Manually wrap training in mlflow.start_run() and call mlflow.log_params() only, since autologging always logs the model artifact.