Summary
This blog post discusses the evolution and practical applications of Ray, an open-source Python framework for distributed execution of AI workloads, originally developed for reinforcement learning. The author, Michael Kennedy, interviews Ray's founding engineers about its journey from academic research to real-world implementation, highlighting its tools and capabilities for enhancing Python applications, especially for large-scale AI projects.