Question

A data scientist trains a scikit-learn RandomForestClassifier inside a notebook. Before calling model.fit(), they run mlflow.sklearn.autolog(). After training completes, they want to programmatically inspect the training metrics and parameters that were automatically captured for that run, without opening the MLflow UI. Which approach retrieves this information correctly?

Accepted Answer

Use mlflow.get_run(run_id) and read the returned Run object's data.metrics and data.params attributes.

Answer

Call mlflow.sklearn.load_model(model_uri) and read the metrics from the loaded model object's .metrics_ attribute.

Answer

Query the cluster's Spark session with spark.read.format('mlflow').load() to return a DataFrame of parameters and metrics.

Answer

Re-run mlflow.sklearn.autolog() a second time, which prints all captured metrics and parameters to the notebook cell output.