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Answer: Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex AI managed datasets to manage dataset versioning.
The question requires tracking and comparing model versions while incorporating dataset versioning. Vertex AI Experiments is the correct service for tracking and comparing model artifacts and versions, as it allows you to log parameters, metrics, and artifacts for different model runs. For dataset versioning, Vertex AI managed datasets provide built-in versioning capabilities, allowing you to track different versions of your datasets and their lineage. Option D correctly pairs these services. Option C is incorrect because Vertex ML Metadata is a lower-level service for tracking metadata but is not the primary managed service for dataset versioning in Vertex AI. Options A and B use incorrect service combinations: TensorBoard is for visualization, not model comparison; Model Monitoring is for production monitoring; and Vertex AI Training doesn't manage dataset versioning.
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You are using Vertex AI to manage your machine learning models and datasets. After updating a model, you need to track and compare the new version with the previous version, including the use of dataset versioning. What steps should you take?
A
Use Vertex AI TensorBoard to visualize the training metrics of the new model version, and use Data Catalog to manage dataset versioning.
B
Use Vertex AI Model Monitoring to monitor the performance of the new model version, and use Vertex AI Training to manage dataset versioning.
C
Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex ML Metadata to manage dataset versioning.
D
Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex AI managed datasets to manage dataset versioning.