Distributed computing made simple
Easily develop and deploy distributed Python and machine learning applications, at any scale.
Webinar | Register now
Learn how to get started with RLlib for scalable reinforcement learning, from training to serving.
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Learn how Ray libraries (eg., Ray Tune, Ray Serve, etc) help you easily scale every step of your ML pipeline — from model training and hyperparameter search to inference and production serving.
Watch the videoRay Core Tutorial | On Demand
New to Ray? Jump-start your learning with this free hands-on tutorial on Ray Core.
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Origins and mission
Modern workloads, especially in machine learning and artificial intelligence, require immense amounts of compute — which is only possible through distributed execution.
However, building and running distributed apps is hard, and remains inaccessible to most developers. Ray was created at the UC Berkeley RISELab to address this challenge and make distributed computing simple, flexible, and accessible to all engineers.
Anyscale platform
Ray simplifies distributed computing. Anyscale simplifies building, running and managing Ray applications. By eliminating infrastructure and cluster management from the equation, Anyscale lets you focus on your application, and reduce time to market.
Productivity
Focus on your applications, and leave the infrastructure management and compute orchestration to us
Reliability
Operated by the creators of Ray, rest easy knowing your Ray clusters are in the best hands.
Scalability
From testing at scale on a multi-terabyte data to deploying to production, access infinite compute with zero code changes
Performance
Our managed Ray platform provides fast access to clusters, letting you tap into scale quickly and efficiently
Ready to get started?
Sign up for our early access program to try out the infinite laptop experience on Anyscale.
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