Scaling AI Systems in Production: A Practical Guide to GPU Infrastructure, Distributed Training, Inference, Reliability, and Cost Optimization
Modern AI systems do not fail because the models are too simple they fail because the infrastructure around them cannot scale reliably.
Scaling AI Systems in Production is a practical, production-focused guide for engineers who need to design, train, serve, monitor, and optimize AI systems beyond the single-GPU stage. It explains the infrastructure decisions that determine whether large AI workloads remain fast, stable, observable, and economically sustainable in real production environments.
Inside, readers will learn how to work with GPU infrastructure, distributed training, FSDP2, tensor and pipeline parallelism, high-performance inference, KV-cache management, model serving, GPU scheduling, autoscaling, capacity planning, observability, SLOs, failure recovery, and cost optimization. The book also explores modern tools and approaches involving PyTorch, Kubernetes, Kueue, vLLM, Ray Serve, OpenTelemetry, and production GPU monitoring.
What makes this book different is its emphasis on engineering decisions rather than tool tutorials. Instead of overwhelming readers with endless framework options or theoretical detail, it explains how to identify bottlenecks, choose the right architecture, recover from failures, control latency, plan capacity, improve GPU utilization, and reduce infrastructure cost.
Whether you are an ML engineer, AI infrastructure engineer, MLOps professional, platform engineer, SRE, DevOps engineer, or senior developer, this book gives you the practical systems knowledge needed to move from experimental AI workloads to resilient production platforms.
If you want to understand not only how to scale AI but how to scale it efficiently, reliably, and responsibly in production this book belongs on your desk.
Customer Reviews
ThriftBooks ® and the ThriftBooks ® logo are registered trademarks of Thrift Books Global, LLC
