LLMs for Modern Software Delivery and DevOps: Applying Large Language Models to Software Delivery and SRE
暫譯: 現代軟體交付與 DevOps 的 LLMs：將大型語言模型應用於軟體交付與 SRE
Huangliang, Gu, Qingzheng, Zheng, Xiaoling, Niu
- 出版商: Packt Publishing
- 出版日期: 2026-07-01
- 售價: $1,890
- 貴賓價: 9.5 折 $1,795
- 語言: 英文
- 頁數: 442
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1807609197
- ISBN-13: 9781807609191
-
相關分類:
Large language model
海外代購書籍(需單獨結帳)
相關主題
商品描述
A practical guide to applying LLMs across the software development and delivery lifecycle, improve development, testing, operations, and project efficiency across modern software organizations.
Key Features:
- Apply LLMs to modern DevOps workflows across development and operations with practical enterprise examples
- Build architectural fluency in GPT, fine-tuning, RAG, and agent-based systems
- Strengthen software delivery pipelines with AI-informed automation and operational intelligence
Book Description:
If you work in DevOps, SRE, platform engineering, software delivery, operations, testing, or security, this book shows how large language models (LLMs) can reduce delivery friction, improve operational visibility, and support more reliable engineering workflows. Written by enterprise digital transformation and delivery specialists, it focuses on moving LLMs beyond isolated experiments into practical software delivery systems.
You will build the LLM foundations needed to understand modern AI systems, including language model evolution, Transformer architecture, GPT-style generation, and efficient fine-tuning techniques such as LoRA and QLoRA. The book then connects these foundations to enterprise-ready patterns such as retrieval-augmented generation (RAG), multi-agent systems, and platform-based AI assistance. Through operations, testing, coding, project management, and cybersecurity scenarios, you will see how LLMs can support log analysis, ticket handling, root cause analysis, test generation, code generation, risk management, and security workflows.
By the end of the book, you will understand how to move from model experimentation to practical AI-assisted delivery, evaluate where LLMs create measurable value across DevOps, SRE, and platform engineering workflows, and recognize the constraints, risks, and governance considerations involved.
What You Will Learn:
- Apply RAG and multi-agent patterns to enterprise software delivery and platform engineering scenarios
- Use LLMs to support operations tasks such as log analysis, ticket handling, incident response, and root cause analysis
- Explore how LLMs can improve software testing, static analysis, vulnerability repair, and test automation workflows
- Apply code LLMs to development workflows, including code generation, completion, review support, and project-level coding tasks
- Use LLMs to support project management, delivery coordination, risk analysis, and cybersecurity workflows
- Evaluate the practical value, risks, and constraints of introducing LLMs into DevOps, SRE, and platform engineering environments
Who this book is for:
This book is for software engineers, DevOps and SRE professionals, QA and security teams, and technical managers who want to apply and operationalize LLMs across the software delivery lifecycle.
Table of Contents
- Introduction to Large Language Models
- The Cornerstone of Large Language Models-Transformer
- From Transformer to ChatGPT33
- Fine-Tuning Techniques for Large Language Models
- Enterprise AI Application Technology- RAG
- Three Foundational Pillars of Software Delivery
- Practical Applications of Large Language Models in Operations Scenarios
- Practical Applications of Large Language Models in Testing Scenarios
- Practical Applications of Large Language Models in Programming Scenarios
- Practical Applications of Large Language Models in Project Management Scenarios
- Practical Applications of Large Language Models in Security Scenarios
商品描述(中文翻譯)
一個實用的指南，介紹如何在軟體開發和交付生命周期中應用大型語言模型（LLMs），提升現代軟體組織的開發、測試、運營和專案效率。
主要特點：
- 在開發和運營的現代 DevOps 工作流程中應用 LLMs，並提供實際的企業範例
- 建立對 GPT、微調、檢索增強生成（RAG）和基於代理的系統的架構流暢性
- 通過 AI 驅動的自動化和運營智慧加強軟體交付管道
書籍描述：
如果您從事 DevOps、SRE、平台工程、軟體交付、運營、測試或安全工作，本書將展示大型語言模型（LLMs）如何減少交付摩擦、改善運營可見性，並支持更可靠的工程工作流程。本書由企業數位轉型和交付專家撰寫，重點在於將 LLMs 從孤立的實驗推向實用的軟體交付系統。
您將建立理解現代 AI 系統所需的 LLM 基礎，包括語言模型的演變、Transformer 架構、GPT 風格生成，以及高效的微調技術，如 LoRA 和 QLoRA。本書然後將這些基礎與企業就緒的模式連接，如檢索增強生成（RAG）、多代理系統和基於平台的 AI 協助。通過運營、測試、編碼、專案管理和網路安全場景，您將看到 LLMs 如何支持日誌分析、工單處理、根本原因分析、測試生成、代碼生成、風險管理和安全工作流程。
在本書結束時，您將了解如何從模型實驗轉向實用的 AI 協助交付，評估 LLMs 在 DevOps、SRE 和平台工程工作流程中創造可衡量價值的地方，並認識到相關的限制、風險和治理考量。
您將學到的內容：
- 將 RAG 和多代理模式應用於企業軟體交付和平台工程場景
- 使用 LLMs 支持運營任務，如日誌分析、工單處理、事件響應和根本原因分析
- 探索 LLMs 如何改善軟體測試、靜態分析、漏洞修復和測試自動化工作流程
- 將代碼 LLMs 應用於開發工作流程，包括代碼生成、補全、審查支持和專案級編碼任務
- 使用 LLMs 支持專案管理、交付協調、風險分析和網路安全工作流程
- 評估將 LLMs 引入 DevOps、SRE 和平台工程環境的實際價值、風險和限制
本書適合對象：
本書適合軟體工程師、DevOps 和 SRE 專業人士、QA 和安全團隊，以及希望在軟體交付生命周期中應用和運作 LLMs 的技術經理。
目錄：
- 大型語言模型簡介
- 大型語言模型的基石 - Transformer
- 從 Transformer 到 ChatGPT
- 大型語言模型的微調技術
- 企業 AI 應用技術 - RAG
- 軟體交付的三大基礎支柱
- 大型語言模型在運營場景中的實用應用
- 大型語言模型在測試場景中的實用應用
- 大型語言模型在編程場景中的實用應用
- 大型語言模型在專案管理場景中的實用應用
- 大型語言模型在安全場景中的實用應用
