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Answer: Create a Vertex AI hyperparameter tuning job.
The correct answer is option C. Vertex AI provides a service for hyperparameter tuning which allows you to specify the hyperparameters you want to optimize, such as learning rate, number of layers, and kernel size. It automatically runs multiple training jobs with different combinations of these hyperparameters to find the configuration that maximizes performance. This approach helps minimize custom code development and infrastructure management by leveraging the capabilities of Vertex AI hyperparameter tuning jobs.
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You have developed a custom model for image classification using a neural network. To improve the model's performance, you need to automatically identify the optimal values for hyperparameters such as learning rate, number of layers, and kernel size. You plan to run multiple jobs in parallel to find the best combination of these hyperparameters. Additionally, you want to minimize the amount of custom code you need to write and reduce the complexity of infrastructure management. What should you do?
A
Train an AutoML image classification model.
B
Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
C
Create a Vertex AI hyperparameter tuning job.
D
Create a Vertex AI pipeline that runs different model training jobs in parallel.