Name: AI-Powered DevOps Engineering: Practical AI Assistance for Reliable Operations (en Inglés)
SKU: 69221978
Price: 36640.00 CLP
Availability: InStock
Author: Lourenço, João
ISBN: 9798161560549

portada AI-Powered DevOps Engineering: Practical AI Assistance for Reliable Operations (en Inglés)

AI-Powered DevOps Engineering: Practical AI Assistance for Reliable Operations (en Inglés)

Formato

Libro Físico

Editorial

Independently published

Autor

Lourenço, João

Idioma

Inglés

N° páginas

348

Encuadernación

Tapa Blanda

ISBN13

9798161560549

AI-Powered DevOps Engineering: Practical AI Assistance for Reliable Operations (en Inglés)

Lourenço, João (Autor) · Independently published · Tapa Blanda

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Reseña del libro "AI-Powered DevOps Engineering: Practical AI Assistance for Reliable Operations (en Inglés)"

AI assistance can help an engineer explain a failed deployment, review an infrastructure change, or search an operational runbook. Using that assistance responsibly requires a clear account of the evidence, the permissions involved, and the checks that must happen before a change reaches a service. AI-Powered DevOps Engineering gives working DevOps engineers, platform engineers, and SREs a practical method for building those checks into everyday work. João Lourenço takes the reader through language-model limitations, security and privacy, prompt and context design, and a reproducible environment for evaluating generated suggestions. The sixteen chapters then apply that method to Linux and Python automation, infrastructure as code, containers, delivery pipelines, observability, incident response, and reliability. Later chapters develop retrieval over operational knowledge, bounded agents with explicit tool permissions, and evaluations of quality, cost, and governance. A capstone brings these ideas together in a DevOps/SRE assistant. Worked examples and answered exercises show how to inspect assumptions, reject unsupported conclusions, and design independent tests. Ten laboratory worksheets use synthetic evidence and deterministic local checks; their companion code is supplied separately. The examples distinguish these local results from integrations that still need testing against live infrastructure and model providers. For readers already comfortable with source control, automation, and infrastructure, this book provides a disciplined foundation for adding AI assistance to engineering work while keeping operational decisions accountable.