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Answer: Set up Vertex AI Experiments to track metrics and parameters. Configure Vertex AI TensorBoard for visualization.
The correct answer is A. Setting up Vertex AI Experiments to track metrics and parameters and configuring Vertex AI TensorBoard for visualization is the most effective approach. Vertex AI Experiments is specifically designed for tracking ML experiments on Google Cloud, simplifying the logging of metrics and parameters without needing custom code. Additionally, Vertex AI seamlessly integrates with TensorBoard, allowing you to visualize training logs and metrics directly within the Experiments interface. This centralized solution minimizes the need for additional code or infrastructure setup compared to other options that involve Cloud Functions, VMs, and manual setup.
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You are part of a team that develops advanced deep learning models using the TensorFlow framework, and you conduct multiple machine learning experiments every week. This frequent experimentation makes it challenging to keep track of all the experiment runs. You need a straightforward solution to efficiently track, visualize, and debug these ML experiment runs on Google Cloud, with minimal additional code or infrastructure overhead. Considering your needs, what would be the most effective approach?
A
Set up Vertex AI Experiments to track metrics and parameters. Configure Vertex AI TensorBoard for visualization.
B
Set up a Cloud Function to write and save metrics files to a Cloud Storage bucket. Configure a Google Cloud VM to host TensorBoard locally for visualization.
C
Set up a Vertex AI Workbench notebook instance. Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.
D
Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.