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AI Infrastructure from Beginner to Advanced: Master the Essential Skills for an AI Infrastructure Engineer: GPUs, Kubernetes, MLOps & Large Language M
Paperback
Publisher: Independently Published
Published: Sep 20 2026
Pages: 548
Weight: 2.76
Height: 1.11 Width: 8.50 Depth: 11.00
Language: English
Build the infrastructure that powers modern AI-from GPUs and Kubernetes to MLOps, distributed training, Generative AI, and production systems.
Hands-On Labs with GitHub and QR Code AccessThis is not just a book to read-it is designed to help you build. Hands-on labs include code, commands, configurations, and exercises in the companion GitHub repository. A QR code is provided for each lab for quick access to its GitHub resources.
AI is transforming nearly every industry, but powerful models are only part of the equation. Production AI requires infrastructure for compute, storage, networking, training, deployment, inference, monitoring, security, reliability, and scalability.
AI Infrastructure from Beginner to Advanced provides a practical, systems-oriented guide to building the infrastructure behind modern AI.
The book progresses from CPUs, GPUs, Linux, cloud, storage, and networking to Docker, Kubernetes, accelerated computing, distributed training, MLOps, production inference, observability, edge AI, Generative AI, and enterprise AI operations.
What You Will LearnLearn to design AI infrastructure; work with CPUs, GPUs, CUDA, accelerators, storage, and high-speed networking; containerize workloads with Docker; orchestrate them with Kubernetes; and understand multi-GPU and distributed computing.
Explore MLOps, MLflow, CI/CD, model deployment and inference, NVIDIA TensorRT and Triton, Prometheus, Grafana, OpenTelemetry, security, high availability, fault tolerance, and cost optimization.
Advanced topics include edge AI, vector databases, RAG, LLM serving, caching, DeepSpeed, ZeRO, Megatron-LM, FlashAttention, PEFT, LoRA, multi-node training, distributed optimizers, enterprise MLOps, model registries, continuous training, drift detection, governance, and compliance.
Learn by BuildingHands-on labs cover GPU environments, Docker, Kubernetes, CUDA, PyTorch, distributed training, MLflow, CI/CD, model serving, monitoring, edge AI, data pipelines, RAG, large-scale training, and enterprise MLOps.
Understand why each component exists, what problem it solves, and how it fits into a complete AI platform.
From a Single GPU to Enterprise AIBegin with infrastructure fundamentals, then progress through Kubernetes, GPU computing, distributed systems, MLOps, production inference, observability, security, and reliability.
Later chapters explore Generative AI infrastructure, LLM serving, vector databases, RAG, multi-node training, distributed optimization, and enterprise MLOps-showing how AI platforms are designed, deployed, scaled, monitored, secured, and operated.
Who This Book Is ForIdeal for AI infrastructure engineers, cloud engineers and architects, DevOps and platform engineers, MLOps and machine learning engineers, data and software engineers, Kubernetes administrators, solutions architects, technical leaders, students, and professionals entering AI infrastructure.
No expertise in every technology is required. The book progresses from foundational concepts to advanced architectures.
Understand AI as a Complete SystemA production AI platform is more than a model. Data pipelines feed training systems, GPUs accelerate computing, distributed frameworks coordinate workloads, MLOps automates releases, inference servers serve models, Kubernetes manages resources, and observability, security, and reliability systems keep platforms operational.
AI Infrastructure from Beginner to Advanced shows how these components work together and provides practical knowledge to build and operate modern AI infrastructure.
Move beyond the model. Learn the infrastructure that makes AI work at scale-and put that knowledge into practice.
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