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MLS-C01 · Question #130

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…

The correct answer is B. Redeploy the model on an M5 instance. Redeploy with CPU and add elastic inference to reduce costs. https://aws.amazon.com/machine-learning/elastic-inference/

Machine Learning Implementation and Operations

Question

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?

Options

  • ARedeploy the model as a batch transform job on an M5 instance.
  • BRedeploy the model on an M5 instance.
  • CRedeploy the model on a P3dn instance.
  • DDeploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3

How the community answered

(25 responses)
  • A
    4% (1)
  • B
    84% (21)
  • C
    4% (1)
  • D
    8% (2)

Explanation

Redeploy with CPU and add elastic inference to reduce costs. https://aws.amazon.com/machine-learning/elastic-inference/

Topics

#SageMaker Endpoints#Resource Optimization#Instance Types#Real-time Inference

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