Ray on Kubernetes
Architecting Scalable Machine Learning Systems
Created by JyotiMany organizations struggle to scale their machine learning operations, often hitting roadblocks when moving from prototype to production. Traditional approaches fall short when dealing with the complexities of distributed computing and resource management. Ray on Kubernetes: Architecting Scalable Machine Learning Systems offers a powerful solution to this challenge. This book provides a deep dive into the integration of Ray, a distributed computing framework, with Kubernetes, the leading container orchestration platform. You'll discover how to design, deploy, and manage large-scale machine learning systems that can handle complex workloads with ease. From setting up your first Ray cluster on Kubernetes to optimizing performance for production environments, this guide covers it all. Packed with practical examples and best practices, you'll learn how to leverage the full potential of distributed computing for your machine learning projects. Whether you're dealing with massive datasets, computationally intensive algorithms, or need to serve models at scale, this book will equip you with the tools and knowledge to build robust, efficient, and scalable machine learning architectures.
Est. Length
269 pages (13 chapters)
Language
English
Publication date
Aug 6, 2025
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