What occurs when an ML pipeline fails during execution?

When an ML pipeline fails during execution, the details page provides the option to restart the pipeline. This feature allows users to quickly address the failure without having to create a new pipeline from scratch. By offering a restart option, it enhances usability and efficiency, enabling users to investigate the issue, make necessary adjustments, and resume operations promptly. This functionality is essential in maintaining the workflow and minimizing downtime, as it allows for iterative improvements based on the pipeline's previous runs.

Other options do not accurately reflect the behavior of a typical ML pipeline in these scenarios. For instance, permanently deleting the pipeline would not be practical, as it would eliminate the possibility of troubleshooting or learning from the failure. Automatic restarts are not standard, as they could lead to repeated failures without allowing for diagnosis and correction. Completely erasing logs would hinder the troubleshooting process, as logs provide vital insights into what went wrong during execution.