Description du livre
Traditional DevOps is struggling with new challenges in today's fast-changing software world. With the rise of microservices, cloud-based systems, and AI-driven automation, managing software has become increasingly difficult. Teams often deal with too many tools, repetitive manual tasks, and slow innovation. NoOps provides a clear guide to using AI to streamline DevOps and reduce manual work.
The book starts by explaining how DevOps has evolved and why software development has become so fragmented. It highlights the importance of standardization as the first step toward NoOps. Readers will learn how AI can improve coding, testing, infrastructure management, and software deployment. It covers AI-powered development tools, automated testing, self-managing infrastructure, and intelligent AI agents that handle deployments and fix problems automatically. Real-world case studies show how companies are already using AI to transform their DevOps processes. Beyond automation, NoOps also explores how AI will change job roles, requiring new skills and shifting how teams work. It discusses ethical concerns, team dynamics, and the future of AI-driven software development.
Whether you're a developer, DevOps engineer, or tech leader, this book will help you understand and prepare for a future where AI plays a major role in software delivery.
What you will learn:
How DevOps has evolved and why traditional methods struggle with modern software challenges.
How AI can automate coding, testing, and infrastructure management to streamline workflows.
Explore AI-driven DevOps strategies, including AI orchestration, self-healing infrastructure, and predictive analytics.
Discover real-world case studies of companies successfully using AI to improve software delivery.
Who this book is for:
Technical Executives, DevOps Engineers & SREs looking to automate testing, monitoring, infrastructure, and CI/CD. Software Developers who want to write better code faster using AI-driven development tools. QA Engineers & Testers responsible for functional, integration, and performance testing who need to automate and self-heal test cases with AI.