Quickly Train NLP Models At Any Scale
Achieving state-of-the-art natural language processing requires compute at an unprecedented scale. More than data, the compute necessary to properly train, tune and serve an effective NLP model can be massive — more than 5x increase every year.
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This is why a scalable compute platform is necessary to enable better, more efficient NLP models that are effectively optimized to deliver the best results. What’s needed are flexible and scalable machine learning platforms that can handle:
Disparate inputs
Unique data types
Varied dependencies
And complex integrations
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud — with no changes.
Why everyone is turning to Ray
Greg Brockman | Co-founder, Chairman, and President
"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."
Shuning Bian | Chief Architect
“Ray and Anyscale have been instrumental in scaling Dendra Systems’ machine learning platform to handle our ever increasing dataset of ultra-high resolution UAV imagery.”
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