{"product_id":"9781807301712","title":"Distributed AI Systems : A practical guide to building scalable training, inference, and serving systems for production AI","description":"\u003cp\u003eLearn distributed AI through hands-on experience with training frameworks, inference engines, and orchestration tools to build production-ready training, inference and serving systems for modern large-scale AI.\u003c\/p\u003e\n\n\u003cp\u003eKey Features\u003c\/p\u003e\n\n\u003cp\u003eUnderstand GPU hardware, high-speed interconnects, and parallelism strategies\n\u003cbr\u003eLearn distributed training with resource-optimized techniques\n\u003cbr\u003eDeploy high-performance inference with advanced optimization and memory management\n\u003cbr\u003eBuild production serving stacks with job schedulers, orchestration, and observability\n\u003cbr\u003ePurchase of the print or Kindle book includes a free PDF eBook\u003c\/p\u003e\n\n\u003cp\u003eBook DescriptionAs AI models grow to billions and trillions of parameters, distributed systems are essential for training and serving them. Many resources cover fragments of this domain, but none provide a full path from distributed training to inference and production deployment. This book fills that gap with practical, production-focused examples.\n\u003cbr\u003eIt starts with GPU and memory estimation, data preparation, and an overview of GPU architecture, interconnects, and core parallelism strategies. You'll learn training techniques including data parallelism for single and multi-node setups, parameter sharding for memory-efficient scaling, and methods to reduce memory usage in large models.\n\u003cbr\u003eThe next section covers distributed inference and deployment. You'll build high-performance systems using optimized attention, caching, operator fusion, and router-based designs. You'll deploy on schedulers and container platforms with GPU-aware orchestration and assemble production stacks emphasizing reliability, scalability, and observability.\n\u003cbr\u003eThe final section covers benchmarking, performance tuning, and trends like MoE models, edge - cloud co-ordination, and advanced parallelism. Each chapter includes tested code and debugging guidance.\n\u003cbr\u003eBy the end, you'll be able to build distributed AI systems that scale from a single GPU to large clusters. What you will learn\u003c\/p\u003e\n\n\u003cp\u003eEstimate memory and compute requirements for training and inference\n\u003cbr\u003eUnderstand GPU hardware, interconnects, and parallelism strategies\n\u003cbr\u003eImplement distributed training with parallel and sharded techniques\n\u003cbr\u003eBuild production inference systems with batching and memory management\n\u003cbr\u003eDeploy via cluster orchestration with optimized GPU scheduling\n\u003cbr\u003eCreate production serving stacks with routing and observability\n\u003cbr\u003eBenchmark distributed systems using industry-standard methodologies\n\u003cbr\u003eExplore emerging model trends, distribution strategies, and future paths\u003c\/p\u003e\n\n\u003cp\u003eWho this book is forThis book is designed for ML engineers, AI researchers, and DevOps professionals who need to train or serve large AI models at scale. Platform engineers, HPC cluster administrators, and cloud architects will also find it valuable for advancing their skill sets.\n\u003cbr\u003eA basic understanding of Python and PyTorch is required to get started. Prior experience with distributed systems, cluster schedulers, or container orchestration is helpful but not necessary - the book introduces these concepts from the ground up, beginning with resource estimation, data preparation, and hardware fundamentals.\u003c\/p\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":49035817287915,"sku":"EB_CP1300_F05-03_SIMS","price":86.13,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9781807301712-1.jpg?v=1788282883","url":"https:\/\/kinokuniya.com.sg\/products\/9781807301712","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}