A data scientist runs 20 training jobs with different hyperparameter combinations and wants to compare their validation loss curves side by side before choosing a model for deployment. Which capability of MLflow supports this workflow directly?
- ALogging model artefacts to the MLflow Model Registry so deployment-ready versions are centralised
- BCalling mlflow.autolog to capture the environment and automatically pick the best-performing run without manual review
- CTagging each run with a parent run ID so MLflow aggregates all child metrics into a single averaged metric entry
- DUsing the MLflow Tracking UI to select multiple runs within an experiment and plot their logged metrics on a shared chart Correct
Why A is wrong: The Model Registry stores versioned models for deployment governance, but it does not provide run-comparison views of metrics across multiple training runs.
Why B is wrong: mlflow.autolog records parameters, metrics and artefacts automatically, but it does not evaluate or select the best run; that judgement remains with the practitioner.
Why C is wrong: Parent-child run nesting organises hierarchical sweeps visually but does not average or aggregate child metrics; each run retains its own independent metric logs.
Why D is correct: MLflow groups runs under a named experiment; the Tracking UI lets you select several runs and render their logged metric histories as overlapping time-series charts, directly enabling side-by-side comparison.