"Distributed Computing with Ray" "Distributed Computing with Ray" is a definitive guide for engineers, architects, and researchers seeking to master the design and implementation of large-scale distributed systems using Ray, the cutting-edge open-source framework. The book begins by establishing a solid foundation in distributed systems fundamentals, introducing key concepts such as system models, scalability, fault tolerance, data consistency, and security. From message-passing and synchronization to microservices and serverless paradigms, readers will gain a deep understanding of the architectural patterns essential for building resilient and performant distributed applications. The core of the book offers a comprehensive exploration of Ray's design, architecture, and abstractions, including its node management, task execution, data handling, and efficient resource scheduling across clusters. Detailed chapters walk through task parallelism, actor-based programming, workflow automation, and advanced data management-empowering readers to leverage Ray for scalable computation and complex data-driven pipelines. Practical guides on performance tuning, debugging, and monitoring ensure that readers can build, operate, and optimize distributed workloads with confidence, whether on-premises or in the cloud. Going beyond foundational theory and tooling, "Distributed Computing with Ray" delivers hands-on instruction for deploying production-grade machine learning and data processing pipelines, integrating seamlessly with popular frameworks like TensorFlow and PyTorch. The book also addresses real-world concerns in cloud-native deployment, DevOps, security, cost management, and disaster recovery. Closing with an in-depth survey of the Ray ecosystem, extensibility options, and the project's future trajectory, this resource equips readers with both the knowledge and practical skills to advance the state of distributed computing in research and industry.