Topic 1 Question 52
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day. The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage. Which solution will meet these requirements?
Create AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.
Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.
Deploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.
Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.
ユーザの投票
コメント(2)
- 正解だと思う選択肢: D👍 1GiorgioGss2024/11/27
- 正解だと思う選択肢: D
Sagemaker endpoint to host ML models; Cloudwatch metrics like CPU for autoscaling. { "TargetValue": 50.0, "CustomizedMetricSpecification": { "MetricName": "CPUUtilization", "Namespace": "/aws/sagemaker/Endpoints", "Dimensions": [ {"Name": "EndpointName", "Value": "my-endpoint" }, {"Name": "VariantName","Value": "my-variant"} ], "Statistic": "Average", "Unit": "Percent" } }
👍 1Saransundar2024/12/04
シャッフルモード