Ray

Ray is an open-source unified framework for scaling AI and Python applications, operating as an AI compute engine that orchestrates infrastructure for diverse distributed workloads across any accelerator. It offers capabilities for distributed training, data processing, model serving, and reinforcement learning, facilitating the development and deployment of large-scale artificial intelligence and generative AI applications with a Python-native interface.

The Ray project originated from research at UC Berkeley, spearheaded by Robert Nishihara, Philipp Moritz, and Ion Stoica, working alongside Professor Michael I. Jordan. Their foundational insight was the critical need for a flexible, unified distributed computing framework to efficiently manage the complex and evolving workloads inherent in emerging AI, which existing systems struggled to accommodate. Anyscale was subsequently founded in 2019 to commercialize and provide enterprise support for the Ray ecosystem.

Developers and organizations leverage Ray to build and scale their machine learning initiatives, addressing issues like sluggish production timelines and inefficient resource allocation for complex data and model architectures. The platform's mission is to democratize distributed programming for AI, empowering a broad range of developers to construct and deploy sophisticated AI systems with greater ease and efficiency.