Data scientists and machine learning engineers use containers to create custom, lightweight environments to train and serve models at scale with deep learning frameworks such as TensorFlow, Apache MXNet, and PyTorch. With containers, developers get consistent environments for development and deployment. In this tech talk, we'll show you how to use AWS Deep Learning Containers to train and serve models at scale with Amazon SageMaker.

Many state of the art deep learning models have hefty compute, storage and power consumption requirements which make them impractical or difficult to use on resource-constrained devices. In this tech talk, you'll learn why Apache MXNet, an open Source library for Deep Learning, is IoT-friendly in many ways. In addition, you'll learn how services like AWS Lambda and AWS Greengrass make it easy to deploy MXNet models on edge devices.