Learn why thousands of organizations use Ray open-source, a unified framework for scalable computing, to speed AI development and scale machine learning and Python workloads and the Anyscale Platform, an enterprise-ready managed Ray platform, to accelerate time-to-market.
Why Ray? Watch Now!
Hear from Weights & Biases and the Ray Team on how to simplify the building, scaling, tracking and monitoring of your AI/ML models.
Ray breaks the $1/TB barrier as the world’s most cost-efficient sorting system
Business Insider on how OpenAI used Ray to train ChatGPT and their largest ML models. More than anyone, OpenAI pushes the limits of scalability in AI.
How Amazon Scales Improves Cost and Performance by 90% on Ray
How Meta Scales Distributed Training of AI Workloads on Ray
Staff Applied AI Engineer
How KocDigital Scales AI and Simplifies AI Development and Ops on Ray
Director of Data and Analytics
How Netflix Scales ML Workloads and Speeds AI Innovation on Ray
Open Source Engineer
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.
The scalable compute platform from the creators of Ray that eases building, deploying, and managing scalable AI and Python applications on Ray.
Data / features
Serving / Applications
Explainability / Observability
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.
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.
Co-founder, Chairman, and President, OpenAI
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.
Senior Manager, Uber AI Platform
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.
Principal Data Scientist
Anyscale is a fully managed scalable Ray compute platform that provides the easiest way to develop, deploy and manage Ray applications.