Niu Xiaoling (6 results)

Condition: New.

Paperback. Condition: New. 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 FeaturesApply LLMs to modern DevOps workflows across development and operations with practical enterprise examplesBuild architectural fluency in GPT, fine-tuning, RAG, and agent-based systemsStrengthen software delivery pipelines with AI-informed automation and operational intelligenceBook DescriptionIf 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 learnApply RAG and multi-agent patterns to enterprise software delivery and platform engineering scenariosUse LLMs to support operations tasks such as log analysis, ticket handling, incident response, and root cause analysisExplore how LLMs can improve software testing, static analysis, vulnerability repair, and test automation workflowsApply code LLMs to development workflows, including code generation, completion, review support, and project-level coding tasksUse LLMs to support project management, delivery coordination, risk analysis, and cybersecurity workflowsEvaluate the practical value, risks, and constraints of introducing LLMs into DevOps, SRE, and platform engineering environmentsWho this book is forThis 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.…

Condition: New.

Paperback. Condition: New. 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 FeaturesApply LLMs to modern DevOps workflows across development and operations with practical enterprise examplesBuild architectural fluency in GPT, fine-tuning, RAG, and agent-based systemsStrengthen software delivery pipelines with AI-informed automation and operational intelligenceBook DescriptionIf 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 learnApply RAG and multi-agent patterns to enterprise software delivery and platform engineering scenariosUse LLMs to support operations tasks such as log analysis, ticket handling, incident response, and root cause analysisExplore how LLMs can improve software testing, static analysis, vulnerability repair, and test automation workflowsApply code LLMs to development workflows, including code generation, completion, review support, and project-level coding tasksUse LLMs to support project management, delivery coordination, risk analysis, and cybersecurity workflowsEvaluate the practical value, risks, and constraints of introducing LLMs into DevOps, SRE, and platform engineering environmentsWho this book is forThis 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.…

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.