Machine Learning on AWS

Machine learning in the hands of every developer and data scientist

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

Whether you are a data scientist, ML researcher, or developer, AWS offers machine learning services and tools tailored to meet your needs and level of expertise.

API-driven ML services

Developers can easily add intelligence to any application with a diverse selection of pre-trained services that provide computer vision, speech, language analysis, and chatbot functionality.

Broad framework support

AWS supports all the major machine learning frameworks, including TensorFlow, Caffe2, and Apache MXNet, so that you can bring or develop any model you choose.

Breadth of compute options

AWS offers a broad array of compute options for training and inference with powerful GPU-based instances, compute and memory optimized instances, and even FPGAs.

Deep platform integrations

ML services are deeply integrated with the rest of the platform including the data lake and database tools you need to run ML workloads. A data lake on AWS gives you access to the most complete platform for big data.

Comprehensive analytics

Choose from a comprehensive set of services for data analysis including data warehousing, business intelligence, batch processing, stream processing, data workflow orchestration.

Secure

Control access to resources with granular permission policies. Storage and database services offer strong encryption to keep your data secure. Flexible key management options allow you to choose whether you or AWS will manage the encryption keys.

Pay-as-you-go

Consume services as you need them and only for the period you use them. AWS pricing has no upfront fees, termination penalties, or long term contracts. The AWS Free Tier helps you get started with AWS.

More machine learning is built on AWS than anywhere else

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Train and deploy models fast

Amazon SageMaker

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. 

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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.

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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.

Fully programmable

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.

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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.

Vision Services

Conversational chatbots

Amazon Rekognition Image

Deep learning-based image analysis

Amazon Rekognition Video

Deep learning-based video analysis

Amazon Lex

Build chatbots to engage customers

Amazon Comprehend

Discover insights and relationships in text

Amazon Translate

Fluent translation of text           

Amazon Transcribe

Automatic speech recognition         

Amazon Polly

Natural sounding text to speech

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.

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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.

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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.

GPU Instances

Nvidia

P3 instances provide up to 14 times better performance than previous-generation Amazon EC2 GPU compute instances. With up to 8 NVIDIA Tesla V100 GPUs, P3 instances provide up to one petaflop of mixed-precision, 125 teraflops of single-precision, and 62 teraflops of double-precision floating point performance.

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Powerful Compute

Intel

C5 instances are powered by 3.0 GHz Intel Xeon Scalable processors, and allow a single core to run up to 3.5 GHz using Intel Turbo Boost Technology. C5 instances offer higher memory to vCPU ratio and deliver 25% improvement in price/performance compared to C4 instances, and are ideal for demanding inference applications. 

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FPGAs On Demand

Amazon EC2 F1 is a compute instance with field programmable gate arrays (FPGAs) that you can program to create custom hardware accelerations for your machine learning applications. F1 instances are easy to program and come with everything you need to develop, simulate, debug, and compile your hardware acceleration code. You can reuse your designs as many times, and across as many F1 instances as you like.

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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

S3

Amazon S3

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.  

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GLUE

AWS Glue

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.

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Analytics services

ATHENA

Amazon Athena

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.

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EMR

Amazon EMR

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.

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REDSHIFT

Amazon Redshift

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.

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REDSHIFT SPECTRUM

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”.

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ML Programs

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.

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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.

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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.

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Amazon Connect

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.

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MACIE

Amazon Macie

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.

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Get Started with machine learning on AWS

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