AI-Native DevOps, DevSecOps, SRE, and Platform Engineering – eBook – PDF

$9.95

AI_Native_Manuscript_Final with coversThis book is my attempt to explain the operating model that I believe is needed for this new period.

DevOps, DevSecOps, Site Reliability Engineering, and Platform Engineering are often treated as separate disciplines, separate teams, or separate transformations. That separation may be convenient for training, conferences, certification, and organizational charts. In real engineering work, the boundaries are not so clean. Flow, security, reliability, platform enablement, governance, and human accountability all meet in the same system. Customers experience the combined result.

That is why this book presents these disciplines as parts of an integrated AI-native operating model.

Description

I wrote this book because I believe software engineering is entering one of the most important transitions of my career.

This book is a companion to the online self-paced course:

AI-Native DevOps, DevSecOps, SRE, and Platform Engineering Course

https://devopsthegray-site-3459.thinkific.com/products/courses/foundations-ai-native-devops

AI is changing how software is imagined, designed, built, tested, secured, delivered, operated, and improved. That statement is easy to say. It is harder to understand responsibly. Many organizations are rushing to adopt AI tools, AI assistants, copilots, and agents. Some of that enthusiasm is justified. AI can help engineers move faster, see patterns earlier, reduce toil, generate useful starting points, and improve how teams learn from evidence.

Speed, however, has never been the same thing as engineering discipline.

I have spent much of my career working where software meets reality: delivery pipelines, test systems, security practices, operational systems, complex platforms, production failures, organizational constraints, and human decision-making under pressure. Again and again, I have seen the same lesson. Software does not succeed because code was written. It succeeds when the delivered system behaves as intended, can be trusted by users, can be operated responsibly, can be secured continuously, and can be improved as conditions change.

That lesson becomes more important in the age of AI.

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