Machine Learning on AWS
Putting machine learning in the hands of every developer
AWS has the broadest and deepest set of machine learning and AI services for your business.
On behalf of our customers, we are focused on solving some of the toughest challenges that hold back machine learning from being in the hands of every developer.
You can choose from pre-trained AI services for computer vision, language, recommendations, and forecasting; Amazon SageMaker to quickly build, train and deploy machine learning models at scale; or build custom models with support for all the popular open-source frameworks.
Our capabilities are built on the most comprehensive cloud platform, optimized for machine learning with high-performance compute, and no compromises on security and analytics.
"AWS is our ML platform of choice, unlocking new ways to deliver on our promise of being the world’s travel platform."
--Matthew Fryer, Hotels.com Vice President and Chief Data Science Officer, Expedia Group
Tens of thousands of customers
More machine learning happens on AWS than anywhere else.
Build, train, and deploy ML fast
Easily add intelligence to your applications
Choice and flexibility with broadest framework support
Fastest and lowest-cost compute options
Comprehensive capabilities, no compromise
Get deep on ML with AWS DeepRacer and DeepLens
Build, train, and deploy machine learning models fast
Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. It removes the complexity that gets in the way of successfully implementing machine learning across use cases and industries—from running models for real-time fraud detection, to virtually analyzing biological impacts of potential drugs, to predicting stolen-base success in baseball.
Amazon SageMaker Studio: Experience the first fully integrated development environment (IDE) for machine learning with Amazon SageMaker Studio, where you can perform all ML development steps. You can quickly upload data, create and share new notebooks, train and tune ML models, move back and forth between steps to adjust experiments, debug and compare results, and deploy and monitor ML models all in a single visual interface, making you much more productive.
Amazon SageMaker Autopilot: Automatically build, train, and tune models with full visibility and control, using Amazon SageMaker Autopilot. It is the industry’s first automated machine learning capability that gives you complete control and visibility into how your models were created and what logic was used in creating these models.
Collaborate faster with Amazon SageMaker Notebooks: Now available in preview, Amazon SageMaker Notebooks provide one-click Jupyter notebooks that you can start working within seconds. Sharing is easy since all code dependencies are automatically captured, so you can easily collaborate with others. Built-in algorithms and deep learning frameworks: Use built-in algorithms and leading deep learning frameworks that have been optimized for scale, accuracy, and performance.
ML marketplace: Choose from hundreds of pre-built algorithms and model available in AWS Marketplace for Machine Learning and use them in Amazon SageMaker.
Reduce labeling costs by up to 70%: Build highly accurate training datasets and reduce data labeling costs by up to 70% using Amazon SageMaker Ground Truth.
One-click training with accuracy: Train your models on a fully managed infrastructure with a single click or by making a single API call. Get maximum accuracy with automatic model tuning to select the best combinations of the hyperparameters from your chosen algorithms.
Experiment management: Organize, track, evaluate, and compare thousands of training runs with Amazon SageMaker Experiments. You can track and manage iterations as the input parameters, configurations, and results are automatically captured allowing you to organize and evaluate many experiments.
Analyze, debug, and fix problems: Amazon SageMaker Debugger removes the opaqueness from the ML training process by automatically capturing real-time metrics during allowing you to improve model accuracy.
One-click deployment: Deploy your trained ML models with a single click or a single API call to start generating predictions for real-time or batch data.
Keep models accurate over time: Use Amazon SageMaker Model Monitor to detect and remediate concept drift, and maintain high quality for your deployed ML models.
Lower machine learning inference costs by up to 75%: Use Amazon Elastic Inference to attach just the right amount of GPU-powered inference acceleration with no code changes, helping you to reduce inference costs by up to 75%.
Easier orchestration with SageMaker Operators for Kubernetes: Use the fully managed capabilities of Amazon SageMaker for your ML infrastructure, and continue to use Kubernetes for orchestration and better control for managing your pipelines.
Easily add intelligence to applications
No machine learning skills required
AWS pre-trained AI Services provide ready-made intelligence for your applications and workflows. AI Services easily integrate with your applications to address common use cases such as personalized recommendations, modernizing your contact center, improving safety and security, and increasing customer engagement. Because we use the same deep learning technology that powers Amazon.com and our ML Services, you get quality and accuracy from continuously-learning APIs. And best of all, AI Services on AWS don't require machine learning experience.
Personalize experiences for your customers with the same recommendation technology used at Amazon.com.
Build accurate forecasting models based on the same machine learning forecasting technology used by Amazon.com.
Image and Video Analysis
Add image and video analysis to your applications to catalog assets, automate media workflows, and extract meaning.
Advanced Text Analytics
Use natural language processing to extract insights and relationships from unstructured text.
Automatically extract text and data from millions of documents in just hours, reducing manual efforts.
Turn text into lifelike speech to give voice to your applications.
Easily build conversational agents to improve customer service and increase contact center efficiency.
Expand your reach through efficient and cost-effective translation to reach audiences in multiple languages.
Easily add high-quality speech-to-text capabilities to your applications and workflows.
Add natural language search capabilities to your apps so your end users can more easily find the information they need.
Identify potentially fraudulent online activities based on the same technology used at Amazon.com.
Automate code reviews and identify your most expensive lines of code.
Choice and flexibility with ML frameworks
Choose from TensorFlow, PyTorch, Apache MXNet, and other popular frameworks to experiment with and customize machine learning algorithms. You can use the framework of your choice as a managed experience in Amazon SageMaker or use the AWS Deep Learning AMIs (Amazon machine images), which are fully configured with the latest versions of the most popular deep learning frameworks and tools.
- 81% of deep learning projects in the cloud run on AWS
- 85% of TensorFlow projects in the cloud run on AWS
- Fastest training for popular deep learning models: AWS-optimized TensorFlow and PyTorch recorded the fastest training time for Mask-RCNN (object detection) and BERT (natural language processing). Learn more »
of TensorFlow projects in the cloud happen on AWS
Get the right compute for any use case
Leverage 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, whether you are training models or running inference on trained models.
- 3x faster network throughput than other providers using P3dn instances
- 25% improvement in price and performance using C5 instances powered by 3.0GHz Intel Xeon compared to previous generation instances
- Custom hardware acceleration using F1 instances with field programmable gate arrays (FPGAs)
- High performance and the lowest cost machine learning inference in the cloud with Inf1 instances
ANALYTICS & SECURITY
Analytics and security for machine learning
In order to do machine learning successfully, you not only need machine learning capabilities, but also the right security, data store, and analytics services to work together. With AWS, you get the most comprehensive capabilities to support your machine learning workloads.
- 99.999999999% durability and unmatched availability using Amazon S3 and Amazon S3 Glacier for storage
- Up to 400% faster data queries using Amazon Redshift for analytics
- Deepest set of security & encryption capabilities
Get deep with machine learning
AWS DeepRacer is a fully autonomous 1/18th-scale race car designed to help you learn about reinforcement learning through autonomous driving.
- Experience the thrill of the race in the real world when you deploy your RL model onto AWS DeepRacer
- Build models in Amazon SageMaker and then train, test, and iterate on the track using the AWS DeepRacer 3D racing simulator
- Starting in 2019, compete in the world’s first global autonomous racing league, to race for prizes and a chance to advance to win the coveted AWS DeepRacer Cup
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 started with deep learning in less than 10 minutes through sample projects with practical, hands-on examples.
- Choose your deep learning model from the AWS DeepLens pre-trained model library, or your own models trained with Amazon SageMaker.
- Deploy your model to the device with a single click.
- Watch the results in real time in the AWS Management Console.
ML PROGRAMS | FOR ORGANIZATIONS
Amazon ML Solutions Lab
The Amazon ML Solutions Lab combines hands-on educational workshops with 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. You'll be able to take what you have learned through the process and use it elsewhere in your organization to apply machine learning to business opportunities.
ML PROGRAMS | FOR RESEARCHERS
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. Our goal is to accelerate the development of innovative algorithms, publications, and source code across a wide variety of ML applications and focus areas.
ML PROGRAMS | DEVELOPERS
Machine Learning Training
Start training on machine learning on AWS with courses based on the same material used to train Amazon's developers through the combination of foundational knowledge and real-world application. Developers, data scientists, data platform engineers, and business decision makers can use this training to learn how to apply ML, AI, and deep learning to their businesses unlocking new insights and value.