Serve Models At Scale
There are four common patterns of machine learning production: pipeline, ensemble, business logic, and online learning. Implementing these patterns typically involves a tradeoff between ease of development and production readiness. Web frameworks are simple and work out of the box but can only provide single predictions; they cannot deliver performance or scale. Custom tooling glue tools together but are hard to develop, deploy, and manage. Specialized systems are great at serving ML models but they are not as flexible or easy to use and can be costly.
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Anyscale helps you go beyond existing model serving limitations with Ray and Ray Serve, which offers scalable, efficient, composable, and flexible serving. Ray Serve provides:
A better developer experience and abstraction
The ability to flexibly compose multiple models and independently scale them
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud — with no changes. Go beyond ML model serving limitations with Ray and Ray serve.
Iterate & Move to Production Fast With Ray Serve & Anyscale
Emiliano Castro, Principal Data Scientist, WildLife
"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."
Greg Brockman, Co-founder, Chairman, & President, OpenAI
"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."
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