Topic 1 Question 283
A mining company wants to use machine learning (ML) models to identify mineral images in real time. A data science team built an image recognition model that is based on convolutional neural network (CNN). The team trained the model on Amazon SageMaker by using GPU instances. The team will deploy the model to a SageMaker endpoint.
The data science team already knows the workload traffic patterns. The team must determine instance type and configuration for the workloads.
Which solution will meet these requirements with the LEAST development effort?
Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Default job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads.
Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Advanced job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads.
Deploy the model to an endpoint by using GPU instances. Use AWS Lambda and Amazon API Gateway to handle invocations from the web. Use open-source tools to perform load testing against the endpoint and to select the best instance type and configuration.
Deploy the model to an endpoint by using CPU instances. Use AWS Lambda and Amazon API Gateway to handle invocations from the web. Use open-source tools to perform load testing against the endpoint and to select the best instance type and configuration.
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コメント(4)
- 正解だと思う選択肢: A
A. Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Default job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads.
Explanation:
SageMaker Model Registry allows you to register and organize your trained models. The SageMaker Inference Recommender Default job type simplifies the process of selecting the best instance type and configuration based on the known traffic pattern. It automatically selects the best instance type for the model. Load testing with the known traffic pattern helps in understanding the actual workloads and selecting the most appropriate instance type and configuration. This approach leverages the capabilities provided by SageMaker without the need for additional infrastructure or open-source tools
👍 1aquanaveen2023/12/17 - 正解だと思う選択肢: B
since traffic patterns are already known, it should be B.
👍 1xiaoeason2023/12/18 - 正解だと思う選択肢: B
It's either A or B. Advanced Job Type recommendations re based on a custom load test where you select your desired ML instances or a serverless endpoint, provide a custom traffic pattern, and provide requirements for latency and throughput based on your production requirements.
👍 1taustin22023/12/19
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