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Answer: Utilize AI Platform to run a hyperparameter tuning job with custom containers, leveraging the pre-trained ResNet model, ensuring scalability and budget efficiency.
The correct approach involves using AI Platform for hyperparameter tuning with custom containers. This method is efficient because it allows for the utilization of the pre-trained ResNet model and facilitates rapid iteration over various parameter combinations to identify the optimal set for model optimization. AI Platform's capability to handle custom containers makes it an ideal choice for this task, ensuring flexibility and scalability in the tuning process, while also being mindful of budget constraints.
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Your data science team is working on optimizing a PyTorch model for image classification using a pre-trained ResNet model. The team is under tight budget constraints and needs to ensure the solution is scalable for future projects. Additionally, the solution must support custom containers for flexibility. What is the most effective approach to perform hyperparameter tuning in this scenario? Choose the best option.
A
Transform the model into a Keras model and initiate a Keras Tuner job for hyperparameter tuning, considering the budget constraints.
B
Utilize AI Platform to run a hyperparameter tuning job with custom containers, leveraging the pre-trained ResNet model, ensuring scalability and budget efficiency.
C
Deploy a Kubernetes Pipelines instance and execute a hyperparameter tuning job using Katib, despite the higher initial setup cost.
D
Convert the model to a TensorFlow model and conduct a hyperparameter tuning job on AI Platform, ignoring the need for custom containers.