Answer-first summary for fast verification
Answer: Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use Vertex AI Experiments to compare different executions of the pipeline. Use Vertex AI TensorBoard to visualize metrics.
Adding a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata and then using Vertex AI Experiments allows for easy comparison of different executions of the pipeline. Vertex AI Experiments provides a user-friendly interface to compare metrics programmatically and visually, making it ideal for collaboration within a team. Additionally, Vertex AI TensorBoard is a powerful tool for visualizing these metrics, further simplifying the process of comparing different pipeline executions. Thus, the correct answer is C.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
No comments yet.
You have created a Vertex AI pipeline that automates custom model training. To enhance team collaboration, you want to add a pipeline component that enables your team to most easily run multiple executions of the pipeline and compare their metrics both visually and programmatically. Which approach should you take?
A
Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Query the table to compare different executions of the pipeline. Connect BigQuery to Looker Studio to visualize metrics.
B
Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Load the table into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.
C
Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use Vertex AI Experiments to compare different executions of the pipeline. Use Vertex AI TensorBoard to visualize metrics.
D
Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.