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. 

Step 1 - Sign up for an AWS account

Sign up for an AWS account

Instantly get access to the AWS services.

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Access Amazon SageMaker

Sign into the Amazon SageMaker console.

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Start building with MXNet

Build your first model with this guide.

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.

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Learn MXNet Gluon in 60-minutes

Use this 60-minute crash course to learn about Gluon, an imperative API for MXNet.

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Create a computer vision application

Try this step-by-step tutorial to build a computer vision application using MXNet.

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Build a language processing application

Use GluonNLP toolkit to easily develop natural language processing models in Gluon.

Explore deep learning on AWS

Amazon SageMaker

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.

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