A team trained a CNN on Amazon SageMaker with GPUs and will deploy it to a SageMaker endpoint for real-time mineral image identification. They already know the expected traffic pattern and need to choose the best instance type and configuration. Which approach requires the least development effort to pick the right instance configuration?

The SageMaker Inference Recommender Advanced job type is the most efficient solution because it automates the process of testing various instance types and configurations against a specified traffic pattern, directly recommending the optimal setup. This significantly reduces development effort compared to manual testing. The Default job type is less comprehensive, offering fewer optimization options. Deploying to an endpoint and using open-source tools for load testing, whether on GPU or CPU instances, requires substantial manual effort for setup, execution, and analysis, making it a less efficient approach for identifying the best instance configuration. Furthermore, the model is a CNN, which typically performs best on GPU instances, making the CPU instance option less suitable from a performance perspective.