AIOps for Network Engineers: A Practical Guide to Automating Monitoring, Incident Response, and Network Operations with AI

Your network isn't broken. Your process for managing it is.

AIOps for Network Engineers shows you exactly how artificial intelligence is changing that equation, not as a vague industry buzzword, but as a set of practical, learnable techniques you can start applying today.

This is a hands-on, no-fluff guide written by a network engineer, for network engineers. It skips the vendor sales pitches and gets straight into how AI-driven monitoring, correlation, and automation actually work in production environments, including when to trust them and when not to.

Inside, you will learn how to:

Replace static, noise-generating thresholds with dynamic baselines that understand your network's real behavior across time of day, day of week, and business cycles

Correlate alerts across devices and services so a single root cause generates one clear, actionable incident instead of fifty disconnected pings

Use AI-assisted troubleshooting to move from symptom to root cause in minutes instead of hours

Build safe, carefully scoped automation for incident response, configuration validation, and routine operational tasks

Predict capacity shortfalls, failing hardware, and performance bottlenecks weeks before they turn into outages

Secure and govern AI-driven automation so it earns your trust instead of becoming a new source of operational risk

Manage the full machine learning model lifecycle behind your AIOps platform, including training, validation, drift detection, and retraining

Design and roll out a genuine AIOps strategy in your own organization, from a scoped pilot through full production deployment

Every chapter is grounded in real, practical scenarios. A failing transceiver caught weeks before it fails completely. A BGP flapping issue traced to its true root cause instead of a plausible but wrong theory. An alert storm from a core switch failure collapsed into a single, clear incident instead of forty separate pages. You will see exactly how these techniques play out when something actually breaks, not just in theory.

This book also covers what most AI and networking content skips entirely: the security, governance, and organizational discipline required to run these systems safely at scale. You will learn how to build least-privilege access for automated systems, audit automated actions properly, avoid over-automation, and keep human engineers firmly in control of the decisions that matter.

Whether you are just starting to explore AIOps, mid-way through an implementation that has stalled, or looking to scale a successful pilot into full production, this book gives you a clear, honest, and technically grounded path forward.

What you will NOT find here is hype. You will not find promises of a fully autonomous network that runs itself, because that is not where the technology genuinely stands today. What you will find is a realistic, evidence-based framework for using AI to reduce alert fatigue, accelerate incident response, catch problems before they escalate, and free your team to focus on the work that actually requires human expertise.

If you are tired of firefighting and ready to build a network operations practice that gets ahead of problems instead of chasing them, this is the guide that gets you there.

Topics covered include AI-powered anomaly detection, event correlation, root cause analysis, automated incident response, network configuration management, predictive capacity planning, performance optimization, MLOps for network models, AIOps security and governance, and a full framework for designing your own AIOps implementation strategy from the ground up.
Stop reacting. Start operating intelligently.