Szczegóły ebooka

With OpenTelemetry, observability has become central to building and operating cloud-native distributed systems. At the same time, advances in AI are transforming how we extract value from the growing volume of observability data. This book shows you how to implement scalable observability, improve engineering efficiency with AI, and extend observability practices from production into development through modern internal developer platforms.

You’ll begin with the fundamentals of observability, logs, metrics, and traces, then learn how AIOps enhances signal correlation, anomaly detection, and root-cause analysis. Through real-world examples and architectural guidance, the book demonstrates how to integrate AIOps into existing systems and build pipelines that proactively detect and resolve issues before users are affected.

You’ll also explore best practices for expanding observability across the software development lifecycle, enabling AI-powered observability as a self-service capability for engineers. Using tools such as OpenTelemetry, Prometheus, Elasticsearch, and Grafana alongside machine learning models, you’ll learn how to automate diagnostics and remediation.

By the end of this book, you’ll be able to design and implement AIOps-enabled observability solutions that make cloud-native systems more resilient and efficient.