The easiest way to get started with building, training, and deploying your deep learning model on Apache MXNet is to use the Amazon SageMaker fully-managed machine learning platform, which comes pre-built with Apache MXNet.
You can also use the AWS Deep Learning AMIs to build custom environments with Apache MXNet. The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale. You can quickly launch Amazon EC2 instances pre-installed with Apache MXNet and Gluon to train sophisticated, custom AI models, experiment with new algorithms, or learn new skills and techniques. Whether you need Amazon EC2 GPU or CPU instances, there is no additional charge for the Deep Learning AMIs – you only pay for the AWS resources needed to store and run your applications.
MXNet tutorials
Get hands-on with these simple deep learning tutorials.
Learn MXNet Gluon in 60-minutes
Use this 60-minute crash course to learn about Gluon, an imperative API for MXNet.
Create a computer vision application
Try this step-by-step tutorial to build a computer vision application using MXNet.
Build a language processing application
Use GluonNLP toolkit to easily develop natural language processing models in Gluon.
Explore deep learning on AWS
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.