MLS-C01 · Question #120
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…
The correct answer is B. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint. 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 endpoint using production variants. Each production variant identifies a machine learning…
Question
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?
Options
- ABuild and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker
- BBuild and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint
- CBuild and host multiple models in Amazon SageMaker Neo to take into account different types of
- DBuild and host multiple models in Amazon SageMaker. Create a single endpoint that accesses
How the community answered
(59 responses)- A5% (3)
- B68% (40)
- C8% (5)
- D19% (11)
Explanation
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 endpoint using production variants. Each production variant identifies a machine learning (ML) model and the resources deployed for hosting the model. To test multiple models by distributing traffic between them, specify the percentage of the traffic that gets routed to each model by specifying the weight for each production variant in the endpoint configuration.
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