Amazon SageMaker
Amazon SageMaker is an everything-as-a-Service - Cloud - machine learning platform of Amazon Web Services , which came in November 2017 the market. SageMaker enables developers to create, train, and deploy machine learning (ML) models in the cloud. SageMaker also enables developers to deploy ML models on embedded systems and edge devices .
power
SageMaker enables developers to work at different levels of abstraction when training and deploying machine learning models. At the highest level of abstraction, SageMaker offers pre-trained ML models that can be used as they are. In addition, SageMaker offers a number of built-in ML algorithms that developers can train on their own data. In addition, SageMaker provides managed instances of TensorFlow and Apache MXNet , in which developers can build their own ML algorithms from scratch. Regardless of the level of abstraction used, a developer can connect their SageMaker-enabled ML models to other AWS services such as B. the Amazon DynamoDB database for structured data storage, AWS Batch for offline batch processing, or Amazon Kinesis for real-time processing.