Which solution will meet these requirements with the LEAST development effort?
Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Default job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads.
Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Advanced job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads.
Deploy the model to an endpoint by using GPU instances. Use AWS Lambda and Amazon API Gateway to handle invocations from the web. Use open-source tools to perform load testing against the endpoint and to select the best instance type and configuration.
Deploy the model to an endpoint by using CPU instances. Use AWS Lambda and Amazon API Gateway to handle invocations from the web. Use open-source tools to perform load testing against the endpoint and to select the best instance type and configuration.
Explanations:
Using the SageMaker Inference Recommender Default job type does not allow for providing detailed traffic patterns for load testing. The Default job type is more general and does not fully utilize the known traffic patterns.
The SageMaker Inference Recommender Advanced job type allows for more customization, including specifying detailed traffic patterns. This solution is optimal for selecting the best instance type and configuration based on known workloads.
While deploying on GPU instances and using AWS Lambda with API Gateway could work, it involves unnecessary complexity with open-source tools for load testing. This approach also lacks integration with SageMaker Inference Recommender, which would provide a more seamless solution.
Deploying on CPU instances is suboptimal for image recognition tasks that rely on CNNs, which typically benefit more from GPU instances. Also, using open-source tools for load testing complicates the process and misses the optimal configuration selection via SageMaker Inference Recommender.
I strategize that the answer is:
Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Advanced job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads.