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Answer: Vertex AI Pipelines, Vertex AI Feature Store, and Vertex AI Experiments
The correct answer is B. Vertex AI Pipelines, Vertex AI Feature Store, and Vertex AI Experiments cover essential aspects of managing an ML workflow. Vertex AI Pipelines automate and orchestrate the training workflow, including data access from different projects. Vertex AI Feature Store manages feature data across different projects, and Vertex AI Experiments allows you to track and compare the performance of different model versions. Dataplex is not directly tied to model versioning and comparison, while Vertex AI TensorBoard is primarily for visualizing training data and metrics. Vertex AI Metadata provides a centralized view of model lineage but does not offer experiment management features like Vertex AI Experiments.
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You are working on an ML project where you need to train models in Vertex AI. Your dataset spans multiple Google Cloud projects, and you need to find, track, and compare the performance of different versions of your models. Which combination of Google Cloud services is best suited to include in your ML workflow to achieve these objectives?
A
Dataplex, Vertex AI Feature Store, and Vertex AI TensorBoard
B
Vertex AI Pipelines, Vertex AI Feature Store, and Vertex AI Experiments
C
Dataplex, Vertex AI Experiments, and Vertex AI ML Metadata
D
Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Metadata