Which architecture changes would ensure that provisioned resources are being utilized effectively?
Redeploy the model as a batch transform job on an M5 instance.
Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
Redeploy the model on a P3dn instance.
Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
Explanations:
Batch transform jobs are suitable for asynchronous processing and not for real-time predictions, which is the current application need.
M5 instances are optimized for general-purpose processing and attaching Amazon Elastic Inference can accelerate GPU tasks cost-effectively, ensuring better resource utilization without overprovisioning.
P3dn instances provide enhanced GPU capabilities, but they may lead to over-provisioning as the current workload does not fully utilize the GPU, leading to unnecessary costs.
Deploying to Amazon ECS with a P3 instance does not address the underutilization issue; it may improve scaling but does not optimize resource usage for lower GPU demand.