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MLS-C01 · Question #164

A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its…

The correct answer is D. Increase the cooldown period for the scale-out activity. This is a problem to do with scaling out (increasing the number of instances), cooldown period should be increased. https://docs.aws.amazon.com/autoscaling/ec2/userguide/Cooldown.html

ML Implementation and Operations

Question

A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible. How can the ML team solve this issue?

Options

  • ADecrease the cooldown period for the scale-in activity.
  • BReplace the current endpoint with a multi-model endpoint using SageMaker.
  • CSet up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
  • DIncrease the cooldown period for the scale-out activity.

How the community answered

(50 responses)
  • A
    12% (6)
  • B
    2% (1)
  • C
    4% (2)
  • D
    82% (41)

Explanation

This is a problem to do with scaling out (increasing the number of instances), cooldown period should be increased. https://docs.aws.amazon.com/autoscaling/ec2/userguide/Cooldown.html

Topics

#SageMaker Auto Scaling#ML Model Deployment#Endpoint Scaling#Cooldown Period

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