Which AWS service provides a fully managed environment for building machine learning models?

Amazon SageMaker is designed specifically as a fully managed platform for building, training, and deploying machine learning models at scale. It simplifies the machine learning workflow by providing built-in algorithms, interactive Jupyter notebooks for data exploration, and various deployment options for endpoints.

With SageMaker, data scientists and developers can focus on the model itself rather than on the underlying infrastructure, as the service handles the heavy lifting of provisioning resources, scaling them as necessary, and managing the entire machine learning lifecycle. This includes everything from data preparation to feature engineering and model evaluation. SageMaker also offers various integrations with other AWS services, enhancing its capabilities for machine learning tasks.

The other options do not provide the same level of specialized support for machine learning. AWS Tools for PowerShell is a set of tools for managing AWS services through scripting. AWS Elemental MediaConvert focuses on video transcoding and processing, while the AWS CLI is a command line interface for managing AWS resources but is not tailored for machine learning tasks. Thus, Amazon SageMaker stands out as the comprehensive solution for building and deploying machine learning models.