Which fully managed service allows a company to build, train, and deploy machine learning models end to end?

1 / 1
Select an answer
CorrectD

Explanation

Question Overview

A question asking to select the fully managed machine learning platform.

Requirements to satisfy
  • 1「build, train, and deploy machine learning models」Full ML lifecycle = SageMaker
  • 2「fully managed」ML platform that requires no infrastructure management
Per-option explanation
AIncorrect

Amazon Bedrock

Amazon Bedrock is a service for using pre-built foundation models through a single API to build generative AI applications.

Covering the entire lifecycle of building, training, and deploying custom models as described in this question is the role of SageMaker, so this is incorrect.

BIncorrect

Amazon Comprehend

Amazon Comprehend is an NLP API service that performs sentiment analysis and key phrase extraction using pre-trained models.

It is a ready-to-use pre-trained API and is not a platform for building, training, and deploying custom models end to end, so this is incorrect.

CIncorrect

AWS Glue

AWS Glue is a serverless ETL service for extracting, transforming, and loading data, and is commonly used to preprocess training data.

However, its role is data preparation, and it is not a service that handles the full lifecycle of building, training, and deploying models, so this is incorrect.

DCorrect

Amazon SageMaker

This is correct. Amazon SageMaker is a fully managed machine learning platform that handles the entire lifecycle from building and training to deploying machine learning models. It supports the full ML lifecycle from data preparation to serving production inference endpoints.

Key Takeaway

'Build, train, and deploy ML models' and 'fully managed ML' point to Amazon SageMaker. Distinguish from Bedrock (uses pre-built foundation models via API) and Comprehend (pre-trained API) by asking 'are you building your own model?'

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