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.