sagemaker enables developers to operate at a number of 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. in addition, sagemaker provides a number of built-in ml algorithms that developers can train on their own data. further, sagemaker provides managed instances of tensorflow and apache mxnet, where developers can create their own ml algorithms from scratch. 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, aws batch for offline batch processing, or amazon kinesis for real-time processing.