The Databricks MLflow experiment UI is designed for traceability and reproducibility. When an MLflow run is initiated from a Databricks notebook, the UI automatically captures a link to that notebook. This link is displayed in the "Source" column on the experiment page. Clicking this link opens a snapshot of the notebook as it existed when the run was executed, allowing users to review the exact code that produced the logged metrics, parameters, and artifacts. This is a key feature for auditing and reproducing machine learning experiments.

A. The MLmodel artifact is a metadata file within the run's artifacts that defines the model's format and dependencies, not a link to the source notebook.

B. The "Models" link or column indicates a logged model artifact and, if registered, links to its page in the Model Registry, not the source code.

D. The "Start Time" link navigates to the detailed page for that specific run, which contains metrics and artifacts, but it is not the direct link to the source notebook from the experiment view.

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1. Databricks Official Documentation

"Organize training runs with MLflow experiments":

Reference: In the section describing the experiment UI table

the documentation states for the "Source" column: "Name of the notebook that created the run... Click the link to view the source." This directly confirms that the "Source" link is the correct method.

Location: https://docs.databricks.com/en/mlflow/experiments.html#view-an-experiment

2. Databricks Official Documentation

"Tutorial: ML end-to-end on Databricks":

Reference: This tutorial guides users through the ML lifecycle on Databricks. In the section "View the experiment and run

" it explicitly instructs the user: "To view the notebook version that created the run

click the link in the Source field." This provides a practical

documented example of the correct procedure.

Location: https://docs.databricks.com/en/machine-learning/end-to-end-example.html#view-the-experiment-and-run