Question

A data scientist has run 30 experiments to tune a gradient boosting classifier, and each run logged parameters and metrics (including validation AUC) to the same MLflow experiment. They now want to programmatically identify the single run with the highest validation AUC so they can register that run's model. Which approach accomplishes this most directly?

Accepted Answer

Use mlflow.search_runs() on the experiment and sort the returned DataFrame by the metrics.val_auc column in descending order, then take the top row's run_id.

Answer

Open each run individually in the MLflow UI and manually copy the run_id of the run with the highest AUC into the registration code.

Answer

Call mlflow.get_run() with no arguments to automatically return the best-performing run in the active experiment.

Answer

Re-run all 30 experiments with autologging enabled so MLflow automatically flags the best run in the experiment metadata.