Topic 1 Question 110
A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users. The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models. Which solution satisfies these requirements with MINIMAL effort?
Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.
Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
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B is the correct answer.
A/B testing with Amazon SageMaker is required in the Exam.
In A/B testing, you test different variants of your models and compare how each variant performs.
Amazon SageMaker enables you to test multiple models or model versions behind the
same endpointusingproduction variants.Each production variant identifies a machine learning (ML) model and the resources deployed for hosting the model.
To test multiple models by
distributing trafficbetween them, specify thepercentage of the trafficthat gets routed to each model by specifying theweightfor eachproduction variantin the endpoint configuration.👍 38SophieSu2021/10/24I would answer B, it seems similar to this AWS example: https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html#model-testing-target-variant
👍 9joep212021/10/17Think anser is D, below is from the Sagemaker doc.
"https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html" Use Batch Transform to Test Production Variants To test different models or various hyperparameter settings, create a separate transform job for each new model variant and use a validation dataset. For each transform job, specify a unique model name and location in Amazon S3 for the output file. To analyze the results, use Inference Pipeline Logs and Metrics.
👍 4anttan2021/12/05
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