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Difficulty: MediumAWS Machine Learning Services

A retail company wants to develop a custom machine learning model to predict customer churn based on historical shopping behavior. The company's data science team needs a fully managed service to prepare datasets, train the model using their custom algorithms, and deploy it to a production-ready endpoint. Which AWS service should the company use to meet these requirements?

  1. A
    Amazon Comprehend
  2. B
    Amazon Rekognition
  3. Amazon SageMakerAnswer
  4. D
    Amazon Lex

Answer

Amazon SageMaker
Amazon SageMaker is correct because it is a fully managed service that enables data scientists and developers to quickly build, train, and deploy custom machine learning models at scale, covering the entire ML workflow from data prep to hosting.

Step-by-Step Solution

1
Identify the core requirement of the scenario.
The scenario requires a fully managed environment to prepare datasets, train a model using custom algorithms, and deploy it to production.
This requirement points to custom model development rather than using a pre-packaged AI service.
2
Evaluate the capabilities of the listed AWS machine learning services.
Amazon SageMaker is the only listed service that manages the complete machine learning lifecycle (build, train, deploy) for custom models.
Services like Comprehend, Rekognition, and Lex are pre-trained AI services that perform specific tasks and do not support custom model training workflows.

Key Concept

Amazon SageMaker is the AWS service used to build, train, and deploy custom machine learning models, whereas other AWS AI services are pre-built and task-specific.
Estimated Time:1m 0s
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Topics

AWS Machine Learning Services

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