Amazon SageMaker

Amazon SageMaker AI is a cloud-based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud. It can be used to deploy ML models on embedded systems and edge-devices. The platform was launched in November 2017. 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. It offers a number of built-in ML algorithms that developers can train on their own data. The platform features managed instances of TensorFlow and Apache MXNet, where developers can create their own ML algorithms from scratch. A developer may also 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. Various interfaces are available for developers to interact with SageMaker. A web API may remotely control a SageMaker server instance.