Deploy trained Keras or TensorFlow models using Amazon SageMaker Amazon Web Services

Amazon SageMaker makes it easier for any developer or data scientist to build, train, and deploy machine learning (ML) models. While it's designed to alleviate the undifferentiated heavy lifting from the full life cycle of ML models, Amazon SageMaker's capabilities can also be used independently of one another; that is, models trained in Amazon SageMaker can be optimized and deployed outside of Amazon SageMaker (or even out of the cloud on mobile or IoT devices at the edge). Conversely, Amazon SageMaker can deploy and host pre-trained models from model zoos, or other members of your team. In this blog post, we'll demonstrate how to deploy a trained Keras (TensorFlow or MXNet backend) or TensorFlow model using Amazon SageMaker, taking advantage of Amazon SageMaker deployment capabilities, such as selecting the type and number of instances, performing A/B testing, and Auto Scaling. Auto Scaling clusters are spread across multiple Availability Zones to deliver high performance and high availability.