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AI-Powered DevOps with LLMs. Applying Large Language Models to Software Delivery and SRE
Gu Huangliang, Zheng Qingzheng, Niu Xiaoling, Che Xin
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If you work in software engineering, DevOps, SRE, or platform teams, this book written by enterprise digital transformation specialists demonstrates how large language models (LLMs) can enhance automation, software delivery, and operational reliability across modern engineering organizations.
To build familiarity, the book begins hands-on with the technical underpinnings of LLMs, including Transformers, GPT architectures, and fine-tuning techniques such as LoRA and QLoRA. It then develops these foundations to demonstrate how retrieval-augmented generation (RAG) and agent-based systems can be embedded into real enterprise workflows. Across development, testing, operations, security, and project management scenarios, you will see how LLMs enhance code generation, automate testing, improve log analysis and incident response, support root cause analysis, and assist in risk-based decision-making.
By the end of the book, you will be able to move from isolated model experimentation to scalable enterprise practice, designing intelligent DevOps and SRE workflows that are efficient, reliable, and strategically aligned.
To build familiarity, the book begins hands-on with the technical underpinnings of LLMs, including Transformers, GPT architectures, and fine-tuning techniques such as LoRA and QLoRA. It then develops these foundations to demonstrate how retrieval-augmented generation (RAG) and agent-based systems can be embedded into real enterprise workflows. Across development, testing, operations, security, and project management scenarios, you will see how LLMs enhance code generation, automate testing, improve log analysis and incident response, support root cause analysis, and assist in risk-based decision-making.
By the end of the book, you will be able to move from isolated model experimentation to scalable enterprise practice, designing intelligent DevOps and SRE workflows that are efficient, reliable, and strategically aligned.
- 1. Introduction to Large Language Models
- 2. The Cornerstone of Large Language Models—Transformer
- 3. From Transformer to ChatGPT33
- 4. Fine-Tuning Techniques for Large Language Models
- 5. Enterprise AI Application Technology— RAG
- 6. Three Foundational Pillars of Software Delivery
- 7. Practical Applications of Large Language Models in Operations Scenarios
- 8. Practical Applications of Large Language Models in Testing Scenarios
- 9. Practical Applications of Large Language Models in Programming Scenarios
- 10. Practical Applications of Large Language Models in Project Management Scenarios
- 11. Practical Applications of Large Language Models in Security Scenarios
- Title:AI-Powered DevOps with LLMs. Applying Large Language Models to Software Delivery and SRE
- Author:Gu Huangliang, Zheng Qingzheng, Niu Xiaoling, Che Xin
- Original title:AI-Powered DevOps with LLMs. Applying Large Language Models to Software Delivery and SRE
- ISBN:9781807609184, 9781807609184
- Date of issue:2026-07-01
- Format:Ebook - EPUB
- Item ID: e_4ue8
- Publisher: Packt Publishing
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