At the time he had no plans to build AI infrastructure, but he was often distracted from his research because of time spent getting his models to run well across the server clusters he used. 'We were doing AI research, but we were bottlenecked by the tooling,' he says. 'We found ourselves spending all of our time managing clusters, wrangling data, and solving distributed systems challenges.'
Ray is an open-source AI compute engine that solves the hard software engineering challenges of scaling AI applications across many machines and GPUs in the cloud. Nishihara says. A developer can start creating a model using the popular Python language on their laptop, then rely on Ray to scale the model up to run on any brand of GPU, CPU, or other accelerator.
'Working on Ray, we've been privileged to help enable businesses to bring AI to production and really begin realizing the potential of AI to build better products and solve important problems in medicine, agriculture, entertainment, transportation, finance, and so much more,' Nishihara says.
'Generative AI took the complexity problem and made it 10 times worse,' Nishihara notes, highlighting the increasing complexity of implementing AI in production environments.
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