Apple's Approach to Scalable Machine Learning Infrastructure on Ray

Learn how Apple engineers build unified, scalable machine learning infrastructure using Ray to orchestrate complex workloads from large-scale batch inference to training models with hundreds of billions of parameters. Discover the architectural decisions behind Apple's Ray-based framework that unifies training, inference, and data processing under a single scalable system designed for reliability, efficiency, and developer productivity. Explore solutions to critical challenges at extreme scale including resource optimization across heterogeneous infrastructure, fault tolerance for long-running high-stakes workloads, low-latency execution for complex operations, and balancing flexibility with ease of use for both research and production teams. Understand how Ray's distributed computing model enables performance and adaptability while maintaining rigor, reliability, and speed in massive ML system operationalization. Gain practical patterns for building scalable ML frameworks on Ray and insights for deploying, managing, and optimizing large heterogeneous AI workloads in production environments.