There are many ways you can retrieve experiments results using the mlflow API (see example if you want to retrieve and display for only a specific model (assuming you have the `model_name`:
best_models = mlflow.search_runs(filter_string=f'tags.model="{model_name}" and attributes.status = "FINISHED" and metrics.F1 > 0', order_by=['metrics.F1 DESC'], max_results=1)
In Databricks my other favorite way to query runs for ad-hoc analytics is via the "mlflow-experiment" format:
df_client = spark.read.format("mlflow-experiment").load()
df_client.createOrReplaceTempView("vw_client")
Following which one can run queries slice/dice results using SparkSQL:
df_model_selector = spark.sql("""SELECT experiment_id, run_id, metrics.auc as AUC, metrics.F1 as F1, artifact_uri
FROM vw_client
WHERE status='FINISHED'
ORDER BY metrics.f1 desc
""")
display(df_model_selector)
