Scale AI and Python Applications Effortlessly

Ray is an open source unified compute framework for scaling ML and Python workloads. With Ray, training, tuning and serving many models or massive models is reduced to minutes. Learn more about Ray, and the Anyscale Platform, an enterprise-ready managed Ray platform.

Breathtaking Scale and Speed

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Instacart trains thousands of demand forecasting models 12x faster.


Holden Karau

How Netflix Scales ML Workloads and Speeds AI Innovation on Ray


Open Source Engineer

Netflix

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Nestlé trains 30,000 forecasting and churn models in under 6 minutes.


Mark Saroufim

How Meta Scales Distributed Training of AI Workloads on Ray


Staff Applied AI Engineer

Meta

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Dow Chemical accelerates their production scheduling optimization by 10x using Ray.


Ayhan Demirci

How KocDigital Scales AI and Simplifies AI Development and Ops on Ray


Director of Data and Analytics

KocDigital

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Uber speeds model tuning by 2x-6x for HPO workloads.


Patrick Ames

How Amazon Scales Improves Cost and Performance by 90% on Ray


Principal Engineer

Amazon

Organizations Using Ray

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Why everyone is turning to Ray

Companies are using Ray to scale ML and Python workloads including everything from data ingest, to preprocessing, hyperparameter tuning, training, and model serving at scale.

  • Easy Scaling
    • Scale from a laptop to thousands of servers
    • Develop and scale in production, with zero code changes
  • A Unified Framework
    • Supports all workloads - data loading, training, tuning, reinforcement learning, model serving
    • Develop, test, and productionize - in one framework
  • Open Platform
    • Integrate with the ML ecosystem, any ML library, data platform & workflow orchestrators
    • Run on any cloud, Kubernetes or on a laptop
  • Accelerated Development
    • Speed development, testing, iterations and instantly scale in production
    • Move faster from development to scaling in production with no re-coding

The enterprise-ready, fully managed Anyscale Platform

Companies are using Ray to scale ML and Python workloads including everything from data ingest, to preprocessing, hyperparameter tuning, training, and model serving at scale.

Orchestration

Experiment management

Hyperparameter Tuning

Training

Data / features

Serving / Applications

Explainability / Observability

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Any cloud
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Why Anyscale vs. Ray

One Unified Scalable Framework

Effortlessly scale all workloads from data loading to training to hyperparamer tuning, to reinforcement learning and model serving. Learn more about all capabilities and the Ray AI Runtime (AIR).

Organizations globally are using Ray and Anyscale for diverse solutions from recommendation systems, to supply-chain logistics optimization to pricing optimization, virtual environment simulations, and more.

What Users are Saying About Ray and Anyscale

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At OpenAI, we are tackling some of the world’s most complex and demanding computational problems. Ray powers our solutions to the thorniest of these problems and allows us to iterate at scale much faster than we could before. As an example, we use Ray to train our largest models, including ChatGPT.

Greg Brockman

Co-founder, Chairman, and President, OpenAI

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Ray and Anyscale empower even the leanest teams to bring AI to production and realize the business potential of AI in record time.

Laure Fouilloux

Head of Data Intelligence, Ricardo

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We chose Ray as the unified compute backend for our machine learning and deep learning platform because it has allowed us to significantly improve performance and fault tolerance, while also reducing the complexity of our technology stack. Ray has brought significant value to our business.

Xu Ning

Senior Manager, Uber AI Platform

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Being able to operate quickly at this massive scale has enabled us to deliver novel solutions towards Dendra Systems’ mission of scaling ecosystem restoration of our biodiverse natural world.

Shuning Bian

Chief Architect, Dendra Systems

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Ray and Anyscale have enabled us to quickly develop, test and deploy a new in-game offer recommendation engine based on reinforcement learning, and subsequently serve those offers 3X faster in production. This resulted in revenue lift and a better gaming experience.

Emiliano Castro

Principal Data Scientist

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Anyscale is a fully managed scalable Ray compute platform that provides the easiest way to develop, deploy and manage Ray applications.