Which feature should you use to review MLflow's tracking results through a UI?

Which feature should you use to review MLflow's tracking results through a UI?

• A. Notebooks
• B. Experiments
• C. Models
• D. Dashboards

Answer: (B)

Reviewing MLflow’s tracking results through a UI is done using the Experiments view. This view groups runs into an experiment, letting you open individual runs to inspect the inputs you logged (parameters), the outputs (metrics), and any artifacts. You can compare multiple runs side by side to see how changes in parameters influence metrics, which helps identify the best-performing configuration. You can also switch between experiments, filter, and sort runs to focus on what matters. Notebooks are for interactive code execution and exploration, not specifically for reviewing tracking results in the UI. Models refers to the model registry aspects of MLflow, which is about saving and deploying models rather than reviewing all tracking results. Dashboards isn’t a standard MLflow Tracking UI feature for reviewing results.

Reviewing MLflow’s tracking results through a UI is done using the Experiments view. This view groups runs into an experiment, letting you open individual runs to inspect the inputs you logged (parameters), the outputs (metrics), and any artifacts. You can compare multiple runs side by side to see how changes in parameters influence metrics, which helps identify the best-performing configuration. You can also switch between experiments, filter, and sort runs to focus on what matters.

Notebooks are for interactive code execution and exploration, not specifically for reviewing tracking results in the UI. Models refers to the model registry aspects of MLflow, which is about saving and deploying models rather than reviewing all tracking results. Dashboards isn’t a standard MLflow Tracking UI feature for reviewing results.