A data scientist wraps a supported-framework training step in an MLflow run and enables MLflow autologging instead of writing manual logging calls. She expects autologging to remove the need for explicit logging code for the standard items it covers. For a typical supported estimator, which set of items does MLflow autologging capture automatically without explicit per-item logging calls?
- AOnly the final evaluation metric, leaving the hyperparameters and the model artefact to be logged by hand.
- BThe raw training dataset rows and the full GPU memory profile of the training process.
- CThe model hyperparameters, the training metrics, and the trained model artefact for the run. Correct
- DOnly descriptive run tags such as the user name and the source file, with no parameters or metrics recorded.
Why A is wrong: It is plausible to assume autologging is minimal, but it also captures hyperparameters and the model artefact, so restricting it to one metric understates what the feature does.
Why B is wrong: Capturing inputs and resource profiles sounds thorough, but autologging does not snapshot raw data rows or detailed GPU memory profiles as part of its standard behaviour.
Why C is correct: Autologging for supported frameworks records the estimator's hyperparameters, the metrics produced during fitting, and the resulting model artefact, which is precisely the standard logging it removes from manual code.
Why D is wrong: Run context tags are recorded by tracking, which makes this tempting, but autologging's purpose is the parameters, metrics, and model, not tags alone.