AI Networking for Beginners - Softcover

Shouse, Sean

9798194786701: AI Networking for Beginners

Softcover

ISBN 13: 9798194786701

Publisher: Independently published, 2026

View all copies of this ISBN edition

0 Used

2 New

From � 50.86

AI is transforming networks—and networks are becoming the foundation that makes modern AI possible.

Are you prepared to work confidently where these two fast-growing fields meet?

AI Networking for Beginners gives you a practical, accessible foundation in both sides of this transformation: using artificial intelligence to operate smarter networks and designing high-performance infrastructure for demanding AI workloads. You do not need an advanced networking certification or machine-learning background to begin.

Inside, you’ll learn how to:

Understand AIOps, predictive maintenance, anomaly detection, and self-healing networks
Use observability, telemetry, and AI-driven analytics to uncover network problems faster
Explore intent-based networking, automation, Zero Trust, and intelligent threat detection
Understand why AI training and inference require specialized network architectures
Navigate leaf-spine fabrics, Clos architecture, lossless Ethernet, and GPU clusters
Compare InfiniBand, RoCE, RDMA, NVLink, and other high-performance interconnects
Recognize collective communication patterns and the causes of distributed-training bottlenecks
Build a practical learning path toward careers in AI infrastructure and network engineering
Reinforce essential terminology with a detailed glossary and acronym reference

Across fifteen structured chapters, the book connects networking fundamentals, machine learning concepts, data center design, security, automation, and high-performance computing. Clear explanations and black-and-white conceptual illustrations help you understand not only what these technologies do, but why they matter.

Whether you are a network engineer entering AI infrastructure, an IT professional preparing for changing responsibilities, or an AI practitioner seeking a stronger understanding of the systems beneath your models, this guide helps you build a connected, technically grounded view of the field.