The question asks how to add a component to a Vertex AI pipeline to facilitate team collaboration and comparison of metrics during different pipeline executions, both visually and programmatically.
The suggested answer is C. This option leverages Vertex AI's built-in tools for logging, comparing, and visualizing metrics, providing a streamlined workflow within the Google Cloud ecosystem.
You have created a Vertex AI pipeline that automates custom model training. You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?
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