[4] SageMaker enables developers to operate at a number of different levels of abstraction when training and deploying machine learning models.
At its highest level of abstraction, SageMaker provides pre-trained ML models that can be deployed as-is.
[5] In addition, it offers a number of built-in ML algorithms that developers can train on their own data.
[6][7] The platform also features managed instances of TensorFlow and Apache MXNet, where developers can create their own ML algorithms from scratch.
[8] Regardless of which level of abstraction is used, a developer can connect their SageMaker-enabled ML models to other AWS services, such as the Amazon DynamoDB database for structured data storage,[9] AWS Batch for offline batch processing,[9][10] or Amazon Kinesis for real-time processing.