You have recently developed a custom model for image classification by using a neural network. You need to automatically identify the values for learning rate, number of layers, and kernel size. To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management. What should you do?
To automatically identify optimal values for learning rate, number of layers, and kernel size without extensive custom code development and infrastructure management, creating a Vertex AI hyperparameter tuning job is the best approach. This service allows you to specify the hyperparameters you want to optimize, and it runs multiple training jobs with different combinations of these hyperparameters to find the best configuration. This minimizes the need for custom code development and manages the scaling and parallel execution of the jobs efficiently.
My Answer: 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, and then it automatically runs multiple training jobs with different combinations of these hyperparameters to find the configuration that maximizes performance.
Leverage Vertex AI Hyperparameter Tuning
why not b?
True that
https://cloud.google.com/vertex-ai/docs/training/using-hyperparameter-tuning