Explore Ray, an open-source distributed framework from U.C. Berkeley's RISELab designed to scale Python applications from laptops to clusters. Learn how Ray addresses performance challenges in ML/AI systems, including heterogeneous task scheduling and state management for hyperparameter tuning, model training, and reinforcement learning simulations. Discover Ray's features for rapid task scheduling, execution, and distributed state management. Compare Ray to other distributed Python libraries and understand when to use it in your projects. Gain insights into Ray's applications in production deployments and open-source systems. Suitable for developers seeking to scale Python applications without extensive distributed systems expertise.