A data scientist runs 20 experiments and wants to identify the run with the highest validation F1 score. Which is the fastest programmatic approach?
Select an answer to reveal the explanation.
Short Explanation and Infographic
search_runs with order_by and max_results=1 is the one-liner that sorts all runs by any metric and returns the winner — no loops, no manual parsing.
Full explanation below image
![Infographic 1024x768 showing mlflow.searchruns best run retrieval: 20 experiment run tiles shown as mini cards with different F1 scores (0.72, 0.85, 0.91, 0.78...). Single code snippet: mlflow.searchruns(orderby=['metrics.valf1 DESC'], maxresults=1). Result table showing winner run highlighted in gold: runid, valf1=0.91, params. Arrow pointing from code to winner. Leaderboard metaphor with podium.](/media/databricks/dbml/DBML-q056-infographic.webp)
Full Explanation
mlflow.search_runs(experiment_ids=[exp_id], order_by=['metrics.val_f1 DESC'], max_results=1) returns a one-row DataFrame containing the run with the highest val_f1. The full pattern: runs_df = mlflow.search_runs(order_by=['metrics.val_f1 DESC'], max_results=1); best_run_id = runs_df.iloc[0]['run_id']; best_f1 = runs_df.iloc[0]['metrics.val_f1']. The search_runs function supports: filter_string (SQL-like), order_by (list of 'metrics.X ASC/DESC', 'params.X', 'attribute.X'), max_results (limit), search_all_experiments (search across all experiments). Looping with get_run is O(n) API calls. mlflow.get_best_run() doesn't exist. Parsing artifact directories bypasses the tracking API and is fragile.