AIF-C01 · Question #71
A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model. The company needs to implement a…
The correct answer is A. Use Amazon SageMaker Serverless Inference to deploy the model. Amazon SageMaker Serverless Inference is the correct solution for deploying an ML model to production in a way that allows a web application to use the model without the need to manage the underlying infrastructure. Amazon SageMaker Serverless Inference provides a fully managed…
Question
A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model. The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure. Which solution will meet these requirements?
Options
- AUse Amazon SageMaker Serverless Inference to deploy the model.
- BUse Amazon CloudFront to deploy the model.
- CUse Amazon API Gateway to host the model and serve predictions.
- DUse AWS Batch to host the model and serve predictions.
How the community answered
(52 responses)- A79% (41)
- B2% (1)
- C6% (3)
- D13% (7)
Explanation
Amazon SageMaker Serverless Inference is the correct solution for deploying an ML model to production in a way that allows a web application to use the model without the need to manage the underlying infrastructure. Amazon SageMaker Serverless Inference provides a fully managed environment for deploying machine learning models. It automatically provisions, scales, and manages the infrastructure required to host the model, removing the need for the company to manage servers or other underlying infrastructure.
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