Apache MXNet is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning.
MXNet includes the Gluon interface that allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and on mobile apps. In just a few lines of Gluon code, you can build linear regression, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization.
You can get started on AWS with a fully-managed MXNet experience with Amazon SageMaker, a platform to build, train, and deploy machine learning models at scale. Or, you can use the AWS Deep Learning AMIs to build custom environments and workflows with TensorFlow and other popular frameworks such as TensorFlow, Caffe, Caffe2, Chainer, PyTorch, Keras, and Microsoft Cognitive Toolkit.
Grab sample code, notebooks, and tutorial content at the GitHub project page.
Benefits of deep learning using MXNet
Ease-of-Use with Gluon
For IoT & the Edge
Flexibility & Choice
MXNet case studies
Amazon SageMaker for machine learning
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.