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

Discover how AIOps is transforming the observability landscape for cloud-native and traditional systems. Learn how to build, monitor, and operate resilient services using AI-drive dynamic insights for smarter and more scalable operationsKey FeaturesBridges observability and AI into a unified operational approach rather than treating them as separate domainsUses a continuous case study to connect concepts across chapters and reflect real-world engineering scenariosFocuses on evolving operational maturity from reactive to proactive and preventive systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionObservability 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.What you will learnBuild observability pipelines for logs, metrics, traces and eventsImplement standards such as OpenTelemetry and PrometheusCorrelate signals from multiple sources for better incident triageApply AI/ML for anomaly detection and root cause analysisDesign scalable architectures for intelligent monitoringAutomate resiliency through self-healing and remediation agentsWho this book is forThis 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.Table of ContentsObservability: The Art of Turning Data into InsightsThe Elephant in the Room: Artificial IntelligenceFrom Observability to AIOps and the Use Cases it Solves TodayACME Financial Services: Implementing AIOpsDemocratizing Observability: A Primer to Self-Service PlatformsThe Observability Agent: Real-Life Use CasesACME Financial Services: How to Move from AIOps to Agentic PlatformsEvolving Operations: Proactive > Preventive > Self-Driven ArchitectureNo Future Without ChallengesACME Financial Services: How Will the AI Future Shape Our Company? Read more