[Farid-Khan]

English | 2026 | ISBN: 1806389592 | 420 pages | True PDF | 17.31 MB

Catergory: Computer Technology, Nonfiction

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 operations Key Features

Practical Integration of AI and Observability in Modern Engineering Workflows
Real-World Use Cases Grounded in Industry Experience
Tailored for Modern Engineering Roles and Organizations Book 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 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 Who this book is 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.
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Contents of Download:

? Observability in the AI-Native Era AIOps Building, observing, and operating resilient systems in the artificial intelligence age.pdf (Hilliary Lipsig;Andreas Grabner;Robert Rati (2026) (17.37 MB)

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⭐️ Observability In The AI Native Era AIOps Building Observing And Operating Resilient Systems In The Artificial Intelligence ✅ (18.38 MB)
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