Topic 1 Question 120
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU. 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.
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B is correct. Redeploy with CPU and add elastic inference to reduce costs. See: https://aws.amazon.com/machine-learning/elastic-inference/
👍 25joep212021/09/27B..>Amazon Elastic Inference (EI) is a resource you can attach to your Amazon EC2 CPU instances to accelerate your deep learning (DL) inference workloads. Amazon EI accelerators come in multiple sizes and are a cost-effective method to build intelligent capabilities into applications running on Amazon EC2 instances.
👍 3mona_mansour2021/10/20- 正解だと思う選択肢: B
Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to EC2 and Sagemaker instances, to reduce the cost of running deep learning inference.
You can choose any CPU instance that is best suited to the overall compute and memory needs of your application, and then separately configure the right amount of GPU-powered inference acceleration. This would allow you to efficiently utilize resources and reduce costs.
👍 3AjoseO2023/02/13
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