The correct answer is Amazon SageMaker. It is designed as an end-to-end machine learning service, which facilitates the entire process from model building to deployment. Amazon Lex, while it is a service that uses machine learning, it is specifically for building conversational interfaces using voice and text. Amazon S3 is a storage service, not a machine learning service. AWS Glue is a data integration service used for preparing and loading data for analytics, not for building machine learning models.
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What are the main features of Amazon SageMaker?
How does Amazon Lex relate to machine learning?
What roles do Amazon S3 and AWS Glue play in machine learning workflows?
This question's topic:
AWS Cloud Practitioner CLF-C02 /
Cloud Technology and Services
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