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Answer: Vertex ML Metadata, Vertex AI Experiments, and Vertex AI TensorBoard
The correct answer is C. This is because Vertex ML Metadata is useful for tracking model training parameters and hyperparameters, as well as metrics and lineage information. Vertex AI Experiments helps in organizing and managing model training runs and visualizes experiment results including metrics and parameter comparisons. Finally, Vertex AI TensorBoard provides detailed visualizations of training metrics and model performance, which is essential for analyzing model behavior at each training epoch. Together, these three Vertex AI services provide a comprehensive solution for tracking, comparing, and visualizing ML model training performance.
Author: LeetQuiz Editorial Team
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You are working for a bank as part of the data science team. Your current task is to develop an ML model that will assist in making loan application decisions. Given the complexity of the model, you need to choose appropriate Vertex AI services to include in your end-to-end workflow. It is essential to track the model’s training parameters and metrics for each training epoch. Additionally, you intend to compare the performance of different model versions to select the best one based on specific metrics. Which Vertex AI services should you use?
A
Vertex ML Metadata, Vertex AI Feature Store, and Vertex AI Vizier
B
Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Vizier
C
Vertex ML Metadata, Vertex AI Experiments, and Vertex AI TensorBoard
D
Vertex AI Pipelines, Vertex AI Feature Store, and Vertex AI TensorBoard
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