Ray lets developers scale their Python and AI applications across multiple machines.
Ray provides a distributed runtime to execute Python and AI applications across a cluster, managing tasks, actors, and objects. It includes AI Libraries like Ray Data for scalable datasets, Ray Train for distributed model training, and Ray Serve for deploying ML models, even for large language models.
Ray lets developers scale their Python and AI applications across multiple machines.
ML engineers and data scientists who need to scale their Python AI applications beyond a single machine should use Ray.