At Amazon, we’ve been investing deeply in artificial intelligence for over 20 years. Machine learning (ML) algorithms drive many of our internal systems. It's also core to the capabilities our customers experience – from the path optimization in our fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, our drone initiative Prime Air, and our new retail experience Amazon Go. This is just the beginning. Our mission is to share our learnings and ML capabilities as fully managed services, and put them into the hands of every developer and data scientist.
Why machine learning on AWS?
Machine Learning for everyone
API-driven ML services
Broad framework support
Breadth of compute options
Deep platform integrations
Train and deploy models fast
Amazon SageMaker enables data scientists and developers to quickly and easily build, train, and deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference. Amazon SageMaker has a modular architecture so that you can use any or all of its capabilities in your existing machine learning workflows.
Get hands-on with AWS DeepLens
AWS DeepLens is the world's first deep-learning enabled video camera for developers. Integrated with Amazon SageMaker and many other AWS services, it allows you to get up and running with deep learning quickly and easily.
A new way to learn
AWS DeepLens allow developers of all skill levels to get started with deep learning in less than 10 minutes through sample projects with practical, hands-on examples.
Using AWS Lambda, it is easy to customize and program AWS DeepLens. Models on DeepLens even run as part of an AWS Lambda function for fast experimentation.
Custom hardware for deep learning
AWS DeepLens is a physical high definition wireless video camera, with custom-built, on-board compute capable of running deep learning inference on sophisticated models in real time.
Custom built for deep learning
Out of the box, DeepLens is pre-installed with an optimized version of Apache MXNet. You can run any deep learning framework on the device, including TensorFlow and Caffe2.
API-driven services bring intelligence to any application
Our intelligent services provide you with the ability to add intelligence to your applications through an API call to pre-trained services rather than reinventing-the-wheel by developing and training your own models.
Develop sophisticated models with any framework
AWS supports every major deep learning framework to provide data scientists and developers with the most open and flexible environment.
Amazon Deep Learning AMIs
The AWS Deep Learning AMIs equip you with the infrastructure and tools to accelerate deep learning in the cloud. The AMIs are pre-installed with Apache MXNet, TensorFlow, PyTorch, the Microsoft Cognitive Toolkit (CNTK), Caffe, Caffe2, Theano, Torch, Gluon, and Keras to train sophisticated, custom AI models. The Deep Learning AMIs let you create managed, auto-scaling clusters of GPUs for large scale training, or run inference on trained models with compute-optimized or general purpose CPU instances.
Developed by AWS and Microsoft, Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components. Developers who are new to machine learning will find this interface more familiar to traditional code, since machine learning models can be defined and manipulated just like any other data structure. More seasoned data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures, all without sacrificing training speed.
Gluon is available in Apache MXNet today, a forthcoming Microsoft Cognitive Toolkit release, and in more frameworks over time.
Harness the right compute for any use case
Machine learning requires a broad set of powerful compute options, ranging from GPUs for compute-intensive deep learning, to FPGAs for specialized hardware acceleration, to high-memory instances for running inference. Amazon EC2 provides a wide selection of instance types optimized to fit machine learning use cases. Instance types comprise varying combinations of CPU, memory, storage, and networking capacity and give you the flexibility to choose the appropriate mix of resources, whether you are training models or running inference on trained models.
Build on top of the most complete platform for big data
In order to do machine learning successfully, you not only need machine learning capabilities, but also the right data store, security, and analytics services to work together.
Data lake services
Amazon S3 is object storage built to store and retrieve any amount of data from anywhere. It is designed to deliver 99.999999999% durability, and stores data for millions of applications used by market leaders in every industry. S3 provides comprehensive security and compliance capabilities that meet even the most stringent regulatory requirements. Amazon S3 is the most supported storage platform available, with the largest ecosystem of ISV solutions and systems integrator partners.
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL.
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
AWS EMR enables you to quickly process vast amounts of unstructured data across dynamically scalable clusters using popular frameworks like Apache Spark, Presto, Hive, and Pig.
Amazon Redshift is a fast, fully managed data warehouse that makes it simple and cost-effective to analyze petabyte-scale data using standard SQL and your existing Business Intelligence (BI) tools.
Amazon Redshift Spectrum
Redshift Spectrum enables you to run Amazon Redshift SQL queries against exabytes of data in Amazon S3 to extend the analytic power of Amazon Redshift to query vast amounts of unstructured data in your Amazon S3 “data lake”.
As part of Amazon's commitment to bring machine learning capabilities into the hands of every developer, data scientist, and researcher, Amazon is proud to offer programs that further the creation of machine learning-based solutions.
Amazon ML Solutions Lab
The Amazon ML Solutions Lab pairs your team with Amazon machine learning experts to prepare data, build and train models, and put models into production. It combines hands-on educational workshops with brainstorming sessions and advisory professional services to help you ‘work backwards’ from business challenges, and then go step-by-step through the process of developing machine learning-based solutions. At the end of the program, you will be able to take what you have learned through the process and use it elsewhere in your organization to apply ML to business opportunities.
Amazon ML Research Grants
The AWS Machine Learning Research Awards program funds university departments, faculty, PhD students, and post-docs that are conducting novel research in machine learning (ML).
Our goal is to accelerate the development of innovative algorithms, publications, and source code across a wide variety of ML applications and focus areas. Selected projects receive unrestricted cash gifts and AWS credits that can be redeemed towards any of our cloud services. Recipients also benefit from training resources and have the opportunity to attend an annual research seminar at our headquarters in Seattle.
Machine learning integrations across the AWS platform
Machine learning at AWS extends far beyond the services specifically designed to create ML applications. Many services across the platform make use of machine learning to enhance the functionality they provide to you.
Amazon Connect, a call center in the cloud, is integrated with Amazon Lex to build conversational voice agents, called chatbots, that can proactively resolve and route incoming customer support calls automatically.
Amazon Macie is a security service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS. Macie provides you with dashboards and alerts that give visibility into how this data is being accessed or moved to mitigate unauthorized access or inadvertent data leaks.