Explore the Ray.jl Julia runtime and client for the Ray compute framework in this conference talk by Curtis Vogt, Dave Kleinschmidt, and Glenn Moynihan. Discover how Ray.jl enables distributed computing for Julia, leveraging the widely-used Ray framework's core concepts of Objects, Tasks, and Actors. Learn about the advantages of Ray for Julia-based distributed computing, including its simple mental model, battle-tested backend features, and language-agnostic API. Delve into the challenges faced during Ray.jl's development, such as interfacing with complex C++ APIs, building binary artifacts, and serializing functions across Julia processes. Gain insights from case studies demonstrating Ray.jl's application in scaling workloads at Beacon Biosignals. Understand the basic functionality provided by Ray.jl and get a glimpse of planned future features for the package.