Observability in the AI-Native Era: Leveraging AIOps to build, observe, and operate resilient systems

Hilliary Lipsig

Andreas Grabner

Robert Rati

$52.99

4.5 (2 Ratings)

Paperback Mar 2026 420 pages 1st Edition

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Key benefits

Bridges observability and AI into a unified operational approach rather than treating them as separate domains

Uses a continuous case study to connect concepts across chapters and reflect real-world engineering scenarios

Focuses on evolving operational maturity from reactive to proactive and preventive systems

Purchase of the print or Kindle book includes a free PDF eBook

Description

Observability is mandatory for building and operating cloud-native distributed systems. Tools like OpenTelemetry have standardized how observability data is sourced, and AI now transforms how we extract value from the vast amounts of observability data generated by modern systems. This book guides you in implementing scalable observability, improving engineering efficiency with AI, and integrating observability throughout the Software Development Lifecycle (SDLC) via modern self-service internal developer platforms. You'll start with observability basics and learn how AIOps enhances signal correlation, anomaly detection, and root-cause analysis. Using real-world examples, the book demonstrates how to implement AIOps, build proactive detection pipelines, and automate diagnostics and remediation. You'll explore best practices for expanding observability using OpenTelemetry, Prometheus, Grafana, Dynatrace, Datadog, and New Relic alongside machine learning models, ensuring your systems are accurate, efficient, and secure. You'll also learn how to benchmark, measure, and secure your AIOps implementation, and gain a practical understanding of software compliance and how it applies to your systems. By the end of this book, you'll be ready to design and deliver AIOps-enabled observability solutions that make cloud-native systems more resilient, efficient, and secure.

Who is this book for?

This book is for Software engineers and engineering leaders working on teams with operational responsibilities, such as platform engineering, site reliability engineering (SRE), DevOps, or application development, who want to integrate AIOps capabilities into their workflows will benefit from this book. If your team is responsible for building and running high-performing, resilient software systems, this book is for you.

What you will learn

Build observability pipelines for logs, metrics, traces and events

Implement standards such as OpenTelemetry and Prometheus

Correlate signals from multiple sources for better incident triage

Apply AI/ML for anomaly detection and root cause analysis

Design scalable architectures for intelligent monitoring

Automate resiliency through self-healing and remediation agents

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Shipping Details

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UK:

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Shipments are not trackable

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Rest of the World:

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Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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N/A May 31, 2026

Feefo Verified review

N/A Apr 17, 2026

I bought this book to deepen my understanding of how observability and AIOps can be applied in real production environments, and it exceeded my expectations. As a Site Reliability Engineer who also works closely with AI‑driven systems, I appreciated how practical and experience‑driven the content is. This is not a marketing book—it’s written by people who clearly understand the day‑to‑day challenges of operating complex, distributed systems. The book does a great job explaining the shift from traditional monitoring to true observability, especially in cloud‑native and microservices architectures. It clearly shows why surface‑level metrics aren’t enough when systems fail in non‑obvious ways, and how combining logs, metrics, traces, and events provides the context needed to troubleshoot real incidents. Many of the scenarios felt familiar, such as latency issues with no obvious resource bottleneck or cascading failures across services. From an AIOps and AI engineering perspective, the authors are refreshingly honest. They acknowledge why early AIOps solutions often failed—mainly due to poor data quality and disconnected tooling—and emphasize that strong observability foundations must come first. I found the examples around anomaly detection, signal correlation, and identifying abnormal workload behavior particularly relevant, as they demonstrate where machine learning genuinely helps rather than promising full automation. Another strong point is the focus on trust, safety, and gradual adoption of AI. Instead of pushing “hands‑off” automation, the book explains how teams can start by using AI for insights and recommendations before moving toward more autonomous actions. This aligns well with how most SRE teams actually work in production environments. The recurring enterprise case study ties everything together and makes the concepts easier to follow over time. The authors also address cost, security, compliance, and organizational roles, which are often ignored but are critical in real‑world implementations. Overall, this book feels like a practical playbook rather than a theoretical overview. I would highly recommend it to SREs, platform engineers, DevOps practitioners, and AI engineers who want a realistic, well‑structured guide to observability and AIOps in modern systems.

Observability in the AI-Native Era: Leveraging AIOps to build, observe, and operate resilient systems

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