Which AWS service is recommended for machine learning model training and deployment?

Which AWS service is recommended for machine learning model training and deployment?

• A. Amazon Athena
• B. Amazon Glue
• C. Amazon SageMaker
• D. Amazon EC2

Answer: (C)

Amazon SageMaker is specifically designed for machine learning tasks, making it the ideal choice for model training and deployment. It offers a fully managed environment that allows developers and data scientists to quickly build, train, and deploy machine learning models at scale. With SageMaker, users have access to a wide range of built-in algorithms as well as the ability to bring their own algorithms and frameworks. It also provides features such as automatic model tuning (hyperparameter optimization), model monitoring, and deployment capabilities through hosting services. This allows teams to focus on developing their algorithms and models rather than managing the underlying infrastructure. In contrast, the other options serve different purposes. Amazon Athena is primarily a serverless interactive query service for analyzing data in Amazon S3 using SQL. Amazon Glue is a fully managed extract, transform, load (ETL) service that is used for preparing data for analysis, but it does not specifically focus on machine learning. Amazon EC2 provides scalable computing capacity in the cloud and can be used for various workloads, including machine learning, but does not offer the specialized tools and services for model training and deployment as SageMaker does.

Amazon SageMaker is specifically designed for machine learning tasks, making it the ideal choice for model training and deployment. It offers a fully managed environment that allows developers and data scientists to quickly build, train, and deploy machine learning models at scale.

With SageMaker, users have access to a wide range of built-in algorithms as well as the ability to bring their own algorithms and frameworks. It also provides features such as automatic model tuning (hyperparameter optimization), model monitoring, and deployment capabilities through hosting services. This allows teams to focus on developing their algorithms and models rather than managing the underlying infrastructure.

In contrast, the other options serve different purposes. Amazon Athena is primarily a serverless interactive query service for analyzing data in Amazon S3 using SQL. Amazon Glue is a fully managed extract, transform, load (ETL) service that is used for preparing data for analysis, but it does not specifically focus on machine learning. Amazon EC2 provides scalable computing capacity in the cloud and can be used for various workloads, including machine learning, but does not offer the specialized tools and services for model training and deployment as SageMaker does.