Modern ML and AI applications require a lot of compute power, which usually means distribution over a cluster of machines, as well as management of distributed state, such as the model parameters being trained. Ray, a high-performance distributed execution framework developed by UC Berkeley's RISELab, is targeted at large-scale machine learning and reinforcement learning applications. Ray's features make it suitable for any Python-based application that needs cluster-wide scalability. Join us for this edition of Meet the Expert with Dean Wampler to see how Ray meets the needs of ML/AI applications-without requiring the skills and DevOps effort typically required for distributed computing. You'll learn how Ray enables distribution of Python applications over a cluster and explore examples of ML libraries that use Ray, allowing data scientists to do their work at scale without a lot of programming. O'Reilly Meet the Expert explores emerging business and technology topics and ideas through a series of one-hour interactive events. You'll engage in a live conversation with experts, sharing your questions and ideas while hearing their unique perspectives, insights, fears, and predictions.