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At Amazon, we’ve been investing deeply in artificial intelligence for over 20 years, and many of the capabilities customers experience are driven by machine learning.’s recommendations engine is driven by machine learning (ML), as are the paths that optimize robotic picking routes in our fulfillment centers. Our supply chain, forecasting, and capacity planning are also informed by ML algorithms. Alexa is fueled by Natural Language Understanding and Automated Speech Recognition deep learning; as is our drone initiative, Prime Air, and the computer vision technology in our new retail experience, Amazon Go. We have thousands of engineers at Amazon committed to machine learning and deep learning, and it’s a big part of our heritage.

Within AWS, we’re focused on bringing that knowledge and capability to you through three layers of the AI stack: Frameworks and Infrastructure with tools like Apache MXNet and TensorFlow, API-driven Services to quickly add intelligence to applications, and Machine Learning Platforms for data scientists.

There are many ways to build intelligent applications and many tools available to build them. AWS supports every major deep learning framework to provide data scientists and developers with the most open and flexible environment.

To help you get started quickly, we provide the AWS Deep Learning AMI (available for Amazon Linux and Ubuntu) so that you can create managed, auto-scaling clusters of GPUs for training and inference at any scale. It is pre-installed with Apache MXNet, TensorFlow, Caffe2 (and Caffe), Theano, Torch, Microsoft Cognitive Toolkit, and Keras; as well as with all major deep learning tools and drivers.  

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them.

More TensorFlow models run on AWS than anywhere else with organizations like Pinterest, UCLA, OpenAI, Expedia, and running production applications with TensorFlow on AWS today.

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Apache MXNet's performance scales exceptionally well, making it an ideal framework for applications in the cloud, IoT, and at the edge.

Nvidia, Carnegie Mellon University,, and Wolfram make use of Apache MXNet today to advance their work in AI.

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Amazon EC2 P2 instances provide powerful Nvidia GPUs to accelerate computations, so that you can train your models in a fraction of the time required by traditional CPUs. After training, Amazon EC2 C4 compute-optimized and M4 general purpose instances, in addition to GPU-based instances, are well suited for running inferences with the trained model.

Additionally, field programmable gate arrays (FPGAs) are available for specialized applications where the requirements of complex machine learning applications are specialized. For those applications, you can leverage the increased flexibility and performance of F1 instances.

You can use the Deep Learning CloudFormation template to easily spin up an elastic cluster of P2 instances using the Deep Learning AMI for larger training requirements

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Cloud Formation Template

Our AI services provide developers with the ability to add intelligence to their applications through an API call to pre-trained services rather than developing and training their own models.

Amazon Lex

Amazon Lex uses the same technology as Amazon Alexa to provide advanced deep learning functionalities of automatic speech recognition (ASR) and natural language understanding (NLU) to enable you to build applications with conversational interfaces, commonly called chatbots.

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Amazon Polly

Amazon Polly is a service that turns text into lifelike speech. Polly lets you create applications that speak in over two dozen languages with a wide variety of natural sounding male and female voices to enable you to build entirely new categories of speech-enabled products.

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Amazon Rekognition

Amazon Rekognition, built on technology used by Amazon Prime Photos to analyze billions of images daily, is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images, as well as search and compare faces between images.

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For developers and data scientists who want to focus on building models, the AI platform services remove the undifferentiated overhead associated with deploying and managing infrastructure for training and hosting.

Amazon Machine Learning

Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.

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Apache Spark on Amazon EMR includes MLlib to deploy scalable machine learning algorithms, or you can use your own libraries. By storing data sets in-memory, Spark can provide great performance for machine learning applications.

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