Prescott Lin (32 results)

Soft cover. Condition: Very Good. Soft cover. MMPB. Light edge wear. Corner bumped. Else, tight and square. Donald Hamilton, Editor. d1821. Original cover price 50 cents. Shelved in plastic. Scarce/rare/hard to find/out of print. WESTERN/SHORTSTORIES.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. Neuware - Building an application on OpenShift is only the beginning. Production environments demand far more: automated delivery, secure configurations, scalable workloads, observability, reliable infrastructure, and engineering practices that can withstand continuous change.OpenShift Application Engineering takes the next step beyond application development and explores the advanced practices required to engineer and operate production-ready applications on OpenShift.Building on the foundations established in the first book, this volume focuses on the engineering systems surrounding modern applications. You'll explore how to automate application delivery, design repeatable deployment workflows, integrate CI/CD practices, manage application configuration securely, improve scalability, monitor workloads, and troubleshoot complex production scenarios.The book emphasizes practical engineering patterns rather than isolated features. You'll learn not only how to implement these capabilities, but also why they matter and how they fit into a broader cloud-native application lifecycle.Inside the Book, You'll Explore: - Advanced application deployment patterns on OpenShift- Container image management and application build strategies- Kubernetes-native automation and configuration management- CI/CD pipelines and automated application delivery- Git-based development and deployment workflows- Secure management of application configuration and credentials- Application scaling, resource management, and workload optimization- Health checks, observability, logging, and troubleshooting- Reliability and resilience strategies for production workloads- Security considerations for containerized applications- Automation patterns for repeatable application operations- Engineering practices for maintaining applications throughout their lifecycleDesigned for developers, DevOps engineers, platform engineers, SREs, and infrastructure-minded application teams, this book bridges the gap between deploying an application and engineering a production-grade application platform.If Book 1 taught you how to build and deploy applications with OpenShift, this volume shows you how to automate, secure, scale, observe, and engineer those applications for real-world production environments.Build beyond deployment. Engineer for production.…

Taschenbuch. Condition: Neu. Neuware - Modern application development increasingly depends on containers, Kubernetes, automation, and cloud-native platforms but understanding how these technologies fit together can be challenging.OpenShift Application Development provides a practical introduction to building and deploying modern applications on OpenShift. Instead of overwhelming you with unnecessary theory, the book focuses on the workflows and concepts developers need to understand to become productive with the platform.Starting with OpenShift and Kubernetes fundamentals, you'll progressively learn how applications are packaged, configured, deployed, exposed, updated, and managed within an OpenShift environment. You'll also explore the relationship between source code, container images, application workloads, services, storage, configuration, and platform resources.Throughout the book, practical examples and hands-on workflows demonstrate how the different pieces fit together, helping you move from a development environment to a functioning application deployment with confidence.Inside the Book, You'll Learn How To: - Understand the architecture and core concepts behind OpenShift- Work with projects, namespaces, workloads, and Kubernetes resources- Build and manage containerized applications- Deploy applications to OpenShift using practical development workflows- Configure applications using environment variables, ConfigMaps, and Secrets- Expose applications through services and routes- Work with persistent application storage- Manage application builds and container images- Update and redeploy applications efficiently- Inspect logs, events, resources, and application status- Diagnose common deployment and runtime problems- Establish development workflows that prepare applications for productionWritten for developers and engineers who prefer practical, implementation-focused learning, this book gives you the foundation required to confidently develop applications on OpenShift.Whether you're new to OpenShift, transitioning from traditional application platforms, or strengthening your Kubernetes skills, OpenShift Application Development provides a clear path from your first deployment to productive cloud-native development.Build it. Deploy it. Understand it. Develop with OpenShift.…

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware - Learn AI-Powered Coding, Prompting, Debugging, and Development Workflows from the Ground UpGitHub Copilot Essentials is a practical, hands-on guide for developers who want to harness the power of AI to write better code, faster. This book takes you from the fundamentals of generative AI to real-world application of GitHub Copilot in everyday software development.You will learn how to set up and use GitHub Copilot effectively, craft high-quality prompts for better code suggestions, and integrate AI assistance into your coding workflow. The book covers essential topics such as code generation, debugging, test writing, and working across multiple programming languages.Beyond basic usage, this guide helps you understand how to write cleaner, more maintainable code with AI support, improve your problem-solving workflow, and reduce repetitive development tasks. You will also explore responsible AI usage, security considerations, and best practices for integrating Copilot into personal and team projects.By the end of this book, you will be confident using GitHub Copilot as a daily development partner capable of accelerating your learning, improving your productivity, and enhancing the quality of your software projects.…

Taschenbuch. Condition: Neu. Neuware - Artificial intelligence is transforming the way modern software is built. Large Language Models (LLMs) have made it possible to create intelligent applications that can reason, retrieve knowledge, interact with external tools, automate complex workflows, and collaborate as autonomous agents. But turning these capabilities into reliable, scalable, production-ready systems requires more than prompting a model it requires AI engineering.AI Engineering with LangChain and LangGraph is a comprehensive, hands-on guide that teaches you how to design, build, deploy, and maintain modern AI applications using two of the most powerful frameworks in the LLM ecosystem: LangChain and LangGraph.Whether you're a Python developer, software engineer, machine learning practitioner, or AI enthusiast, this book provides a structured learning path from the fundamentals of LLM application development to advanced agent architectures and enterprise deployment. Through practical examples, real-world projects, and industry best practices, you'll gain the skills needed to build intelligent systems that are robust, scalable, and ready for production.Inside this book, you'll learn how to: - Understand the foundations of AI engineering and modern LLM application architecture.- Build intelligent applications with LangChain's modular components, prompts, models, chains, and runnables.- Create Retrieval-Augmented Generation (RAG) systems that combine LLMs with external knowledge for more accurate and context-aware responses.- Design powerful AI agents capable of reasoning, planning, and using external tools and APIs.- Develop stateful workflows and orchestrate complex agent behavior using LangGraph.- Build collaborative multi-agent systems for research, automation, and decision-making.- Implement memory, conversation management, and long-term knowledge for personalized AI experiences.- Evaluate, monitor, debug, and optimize AI applications using modern observability and testing techniques.- Deploy secure, scalable AI services for real-world production environments.- Apply proven design patterns, architecture principles, and best practices used in professional AI engineering.Throughout the book, you'll work on practical projects that reinforce every concept, helping you move beyond theory and develop the confidence to build sophisticated AI-powered solutions from the ground up.By the end of this book, you'll have the knowledge and practical experience to create enterprise-grade AI applications that leverage LangChain and LangGraph to solve real-world problems equipping you with the skills needed to thrive in the rapidly evolving field of AI engineering.Whether you're building intelligent assistants, knowledge systems, autonomous agents, or next-generation AI products, this book will serve as your complete guide to mastering AI engineering with LangChain and LangGraph.…

Taschenbuch. Condition: Neu. Neuware - Building a Knowledge Graph is only the beginning.Modern AI applications increasingly need to retrieve information across connected entities, reason over multiple relationships, coordinate tools and data sources, and operate reliably at production scale. Advanced Knowledge Graphs for LLMs explores the techniques and architectures required to take graph-powered LLM applications beyond basic retrieval and into sophisticated intelligent systems.This volume builds on the foundations of Knowledge Graph construction and LLM integration to explore advanced retrieval, reasoning, agentic workflows, optimization, evaluation, security, and production deployment.You will learn how to: - Design advanced graph-based retrieval architectures- Build Graph RAG systems for complex information needs- Implement multi-hop retrieval and relationship-aware reasoning- Combine graph search, vector search, and semantic retrieval- Improve context selection and reduce irrelevant information- Build LLM-powered agents that interact with Knowledge Graphs- Design agentic workflows for research, question answering, and decision support- Optimize graph queries, retrieval pipelines, and LLM context windows- Evaluate Knowledge Graph quality and LLM application performance- Develop testing, benchmarking, human-in-the-loop, and A/B evaluation strategies- Address security, access control, privacy, and data governance- Design scalable architectures for large Knowledge Graph and LLM workloads- Monitor, maintain, and continuously improve graph-powered AI systems- Transition experimental Knowledge Graph applications into production environmentsThe book focuses on the engineering challenges that emerge when Knowledge Graphs and LLMs move from prototypes to real-world systems.Through practical architectures, implementation patterns, evaluation strategies, and production considerations, you will learn how to build AI applications that can retrieve connected knowledge, reason across relationships, use structured context, and operate reliably at scale.If Foundations of Knowledge Graphs for LLMs teaches you how to build the foundation, this volume teaches you how to extend, optimize, evaluate, and productionize it.Go beyond basic RAG. Build AI systems that can connect knowledge, retrieve context, and reason across relationships. …

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware - Large language models are powerful but they do not inherently possess a reliable, structured representation of the world. They can struggle with factual consistency, complex relationships, long-context retrieval, and explaining where their answers come from.Foundations of Knowledge Graphs for LLMs provides a practical introduction to designing and building Knowledge Graph systems that give LLM applications structured, connected, and meaningful context.Rather than treating Knowledge Graphs as an isolated database technology, this book explores how graphs can become an essential component of modern AI architectures.You will learn how to: - Understand the core concepts behind Knowledge Graphs and knowledge representation- Design graph schemas, entities, relationships, properties, and ontologies- Work with structured and unstructured data as sources for graph construction- Extract entities and relationships from documents using LLMs and modern NLP techniques- Build Knowledge Graph construction pipelines- Integrate Knowledge Graphs with Large Language Models- Connect graph retrieval with Retrieval-Augmented Generation architectures- Improve contextual grounding and reduce unsupported LLM responses- Implement graph-based retrieval and context assembly strategies- Design systems capable of following relationships across connected information- Understand graph databases, embeddings, vector search, and hybrid retrieval- Build practical Python-based Knowledge Graph and LLM workflows- Design architectures that can evolve from prototypes into scalable AI systemsThe book combines foundational concepts with practical implementation patterns, helping readers understand not only what Knowledge Graphs are, but also how and why they fit into modern LLM applications.Whether you are new to Knowledge Graphs or already working with LLMs and RAG systems, this volume provides the foundation needed to design context-aware AI applications built around structured knowledge.Build the foundation. Structure the knowledge. Give your LLMs better context.…

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Artificial intelligence is transforming the way modern software is built. Large Language Models (LLMs) have made it possible to create intelligent applications that can reason, retrieve knowledge, interact with external tools, automate complex workflows, and collaborate as autonomous agents. But turning these capabilities into reliable, scalable, production-ready systems requires more than prompting a model it requires AI engineering.AI Engineering with LangChain and LangGraph is a comprehensive, hands-on guide that teaches you how to design, build, deploy, and maintain modern AI applications using two of the most powerful frameworks in the LLM ecosystem: LangChain and LangGraph.Whether you're a Python developer, software engineer, machine learning practitioner, or AI enthusiast, this book provides a structured learning path from the fundamentals of LLM application development to advanced agent architectures and enterprise deployment. Through practical examples, real-world projects, and industry best practices, you'll gain the skills needed to build intelligent systems that are robust, scalable, and ready for production.Inside this book, you'll learn how to: Understand the foundations of AI engineering and modern LLM application architecture.Build intelligent applications with LangChain's modular components, prompts, models, chains, and runnables.Create Retrieval-Augmented Generation (RAG) systems that combine LLMs with external knowledge for more accurate and context-aware responses.Design powerful AI agents capable of reasoning, planning, and using external tools and APIs.Develop stateful workflows and orchestrate complex agent behavior using LangGraph.Build collaborative multi-agent systems for research, automation, and decision-making.Implement memory, conversation management, and long-term knowledge for personalized AI experiences.Evaluate, monitor, debug, and optimize AI applications using modern observability and testing techniques.Deploy secure, scalable AI services for real-world production environments.Apply proven design patterns, architecture principles, and best practices used in professional AI engineering.Throughout the book, you'll work on practical projects that reinforce every concept, helping you move beyond theory and develop the confidence to build sophisticated AI-powered solutions from the ground up.By the end of this book, you'll have the knowledge and practical experience to create enterprise-grade AI applications that leverage LangChain and LangGraph to solve real-world problems equipping you with the skills needed to thrive in the rapidly evolving field of AI engineering.Whether you're building intelligent assistants, knowledge systems, autonomous agents, or next-generation AI products, this book will serve as your complete guide to mastering AI engineering with LangChain and LangGraph. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Building an application on OpenShift is only the beginning. Production environments demand far more: automated delivery, secure configurations, scalable workloads, observability, reliable infrastructure, and engineering practices that can withstand continuous change.OpenShift Application Engineering takes the next step beyond application development and explores the advanced practices required to engineer and operate production-ready applications on OpenShift.Building on the foundations established in the first book, this volume focuses on the engineering systems surrounding modern applications. You'll explore how to automate application delivery, design repeatable deployment workflows, integrate CI/CD practices, manage application configuration securely, improve scalability, monitor workloads, and troubleshoot complex production scenarios.The book emphasizes practical engineering patterns rather than isolated features. You'll learn not only how to implement these capabilities, but also why they matter and how they fit into a broader cloud-native application lifecycle.Inside the Book, You'll Explore: Advanced application deployment patterns on OpenShiftContainer image management and application build strategiesKubernetes-native automation and configuration managementCI/CD pipelines and automated application deliveryGit-based development and deployment workflowsSecure management of application configuration and credentialsApplication scaling, resource management, and workload optimizationHealth checks, observability, logging, and troubleshootingReliability and resilience strategies for production workloadsSecurity considerations for containerized applicationsAutomation patterns for repeatable application operationsEngineering practices for maintaining applications throughout their lifecycleDesigned for developers, DevOps engineers, platform engineers, SREs, and infrastructure-minded application teams, this book bridges the gap between deploying an application and engineering a production-grade application platform.If Book 1 taught you how to build and deploy applications with OpenShift, this volume shows you how to automate, secure, scale, observe, and engineer those applications for real-world production environments.Build beyond deployment. Engineer for production. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Modern application development increasingly depends on containers, Kubernetes, automation, and cloud-native platforms but understanding how these technologies fit together can be challenging.OpenShift Application Development provides a practical introduction to building and deploying modern applications on OpenShift. Instead of overwhelming you with unnecessary theory, the book focuses on the workflows and concepts developers need to understand to become productive with the platform.Starting with OpenShift and Kubernetes fundamentals, you'll progressively learn how applications are packaged, configured, deployed, exposed, updated, and managed within an OpenShift environment. You'll also explore the relationship between source code, container images, application workloads, services, storage, configuration, and platform resources.Throughout the book, practical examples and hands-on workflows demonstrate how the different pieces fit together, helping you move from a development environment to a functioning application deployment with confidence.Inside the Book, You'll Learn How To: Understand the architecture and core concepts behind OpenShiftWork with projects, namespaces, workloads, and Kubernetes resourcesBuild and manage containerized applicationsDeploy applications to OpenShift using practical development workflowsConfigure applications using environment variables, ConfigMaps, and SecretsExpose applications through services and routesWork with persistent application storageManage application builds and container imagesUpdate and redeploy applications efficientlyInspect logs, events, resources, and application statusDiagnose common deployment and runtime problemsEstablish development workflows that prepare applications for productionWritten for developers and engineers who prefer practical, implementation-focused learning, this book gives you the foundation required to confidently develop applications on OpenShift.Whether you're new to OpenShift, transitioning from traditional application platforms, or strengthening your Kubernetes skills, OpenShift Application Development provides a clear path from your first deployment to productive cloud-native development.Build it. Deploy it. Understand it. Develop with OpenShift. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. GitHub Copilot Beyond Coding is an advanced guide for experienced developers, tech leads, and engineering teams who want to extend AI beyond writing code into full-scale software delivery and organizational transformation.This book explores how GitHub Copilot can be applied across the entire development lifecycle, including architecture design, code reviews, automated testing, DevOps pipelines, and infrastructure management. It also shows how AI can improve collaboration between developers, QA engineers, product managers, and DevOps teams.You will learn how to streamline pull requests, enhance code quality through AI-assisted reviews, automate repetitive engineering workflows, and improve CI/CD processes. The book also covers scaling Copilot across teams, establishing governance frameworks, measuring productivity impact, and building an AI-first engineering culture.By the end of this book, you will understand how to move beyond individual productivity gains and leverage GitHub Copilot as a strategic tool for transforming how entire engineering organizations build, ship, and maintain software. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Artificial intelligence is transforming the way modern software is built. Large Language Models (LLMs) have made it possible to create intelligent applications that can reason, retrieve knowledge, interact with external tools, automate complex workflows, and collaborate as autonomous agents. But turning these capabilities into reliable, scalable, production-ready systems requires more than prompting a model it requires AI engineering.AI Engineering with LangChain and LangGraph is a comprehensive, hands-on guide that teaches you how to design, build, deploy, and maintain modern AI applications using two of the most powerful frameworks in the LLM ecosystem: LangChain and LangGraph.Whether you're a Python developer, software engineer, machine learning practitioner, or AI enthusiast, this book provides a structured learning path from the fundamentals of LLM application development to advanced agent architectures and enterprise deployment. Through practical examples, real-world projects, and industry best practices, you'll gain the skills needed to build intelligent systems that are robust, scalable, and ready for production.Inside this book, you'll learn how to: Understand the foundations of AI engineering and modern LLM application architecture.Build intelligent applications with LangChain's modular components, prompts, models, chains, and runnables.Create Retrieval-Augmented Generation (RAG) systems that combine LLMs with external knowledge for more accurate and context-aware responses.Design powerful AI agents capable of reasoning, planning, and using external tools and APIs.Develop stateful workflows and orchestrate complex agent behavior using LangGraph.Build collaborative multi-agent systems for research, automation, and decision-making.Implement memory, conversation management, and long-term knowledge for personalized AI experiences.Evaluate, monitor, debug, and optimize AI applications using modern observability and testing techniques.Deploy secure, scalable AI services for real-world production environments.Apply proven design patterns, architecture principles, and best practices used in professional AI engineering.Throughout the book, you'll work on practical projects that reinforce every concept, helping you move beyond theory and develop the confidence to build sophisticated AI-powered solutions from the ground up.By the end of this book, you'll have the knowledge and practical experience to create enterprise-grade AI applications that leverage LangChain and LangGraph to solve real-world problems equipping you with the skills needed to thrive in the rapidly evolving field of AI engineering.Whether you're building intelligent assistants, knowledge systems, autonomous agents, or next-generation AI products, this book will serve as your complete guide to mastering AI engineering with LangChain and LangGraph. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Learn AI-Powered Coding, Prompting, Debugging, and Development Workflows from the Ground UpGitHub Copilot Essentials is a practical, hands-on guide for developers who want to harness the power of AI to write better code, faster. This book takes you from the fundamentals of generative AI to real-world application of GitHub Copilot in everyday software development.You will learn how to set up and use GitHub Copilot effectively, craft high-quality prompts for better code suggestions, and integrate AI assistance into your coding workflow. The book covers essential topics such as code generation, debugging, test writing, and working across multiple programming languages.Beyond basic usage, this guide helps you understand how to write cleaner, more maintainable code with AI support, improve your problem-solving workflow, and reduce repetitive development tasks. You will also explore responsible AI usage, security considerations, and best practices for integrating Copilot into personal and team projects.By the end of this book, you will be confident using GitHub Copilot as a daily development partner capable of accelerating your learning, improving your productivity, and enhancing the quality of your software projects. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Building a Knowledge Graph is only the beginning.Modern AI applications increasingly need to retrieve information across connected entities, reason over multiple relationships, coordinate tools and data sources, and operate reliably at production scale. Advanced Knowledge Graphs for LLMs explores the techniques and architectures required to take graph-powered LLM applications beyond basic retrieval and into sophisticated intelligent systems.This volume builds on the foundations of Knowledge Graph construction and LLM integration to explore advanced retrieval, reasoning, agentic workflows, optimization, evaluation, security, and production deployment.You will learn how to: Design advanced graph-based retrieval architecturesBuild Graph RAG systems for complex information needsImplement multi-hop retrieval and relationship-aware reasoningCombine graph search, vector search, and semantic retrievalImprove context selection and reduce irrelevant informationBuild LLM-powered agents that interact with Knowledge GraphsDesign agentic workflows for research, question answering, and decision supportOptimize graph queries, retrieval pipelines, and LLM context windowsEvaluate Knowledge Graph quality and LLM application performanceDevelop testing, benchmarking, human-in-the-loop, and A/B evaluation strategiesAddress security, access control, privacy, and data governanceDesign scalable architectures for large Knowledge Graph and LLM workloadsMonitor, maintain, and continuously improve graph-powered AI systemsTransition experimental Knowledge Graph applications into production environmentsThe book focuses on the engineering challenges that emerge when Knowledge Graphs and LLMs move from prototypes to real-world systems.Through practical architectures, implementation patterns, evaluation strategies, and production considerations, you will learn how to build AI applications that can retrieve connected knowledge, reason across relationships, use structured context, and operate reliably at scale.If Foundations of Knowledge Graphs for LLMs teaches you how to build the foundation, this volume teaches you how to extend, optimize, evaluate, and productionize it.Go beyond basic RAG. Build AI systems that can connect knowledge, retrieve context, and reason across relationships. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Large Language Models (LLMs) are transforming how software is built, how businesses automate workflows, and how people interact with artificial intelligence. From intelligent chatbots and coding assistants to content generation, search, and reasoning systems, LLMs have become the foundation of modern AI. Yet many developers use these powerful models without truly understanding what happens behind the scenes.Mastering Large Language Models from First Principles takes you beyond APIs and prebuilt frameworks to reveal the engineering concepts that power today's transformer-based language models. Through a practical, hands-on approach, you'll learn how every major component of an LLM works from tokenization and embeddings to self-attention, transformer architectures, pretraining, fine-tuning, evaluation, and deployment.Rather than treating LLMs as black boxes, this book guides you through the underlying mathematics, algorithms, and implementation techniques that make them possible. You'll build essential model components step by step using Python and PyTorch, gaining the confidence to understand, customize, optimize, and extend modern language models for your own applications.As you progress, you'll explore not only the fundamentals but also the techniques used in real-world AI systems, including efficient training strategies, parameter-efficient fine-tuning, retrieval-augmented generation (RAG), model optimization, inference acceleration, and production deployment. Each chapter combines clear explanations with practical coding examples, diagrams, and hands-on projects that reinforce your understanding and help you develop production-ready skills.By the end of this book, you won't just know how to use Large Language Models you'll understand how they are built, how they learn, why they work, and how to adapt them to solve real-world problems across a wide range of industries.What You'll LearnUnderstand the mathematics and engineering principles behind Large Language Models.Build transformer architectures from first principles using Python and PyTorch.Implement tokenization, embeddings, positional encoding, and self-attention mechanisms.Design, train, and evaluate transformer-based language models.Fine-tune pretrained LLMs for classification, instruction following, and domain-specific tasks.Apply parameter-efficient fine-tuning techniques such as LoRA and QLoRA.Build Retrieval-Augmented Generation (RAG) systems that combine language models with external knowledge.Optimize models through quantization, efficient inference, and performance tuning.Deploy LLM-powered applications for real-world use cases.Gain the practical skills needed to confidently work with modern AI technologies and future transformer-based architectures.Whether you're a software developer, machine learning engineer, data scientist, AI practitioner, or an experienced Python programmer eager to deepen your understanding of generative AI, this book provides the theoretical foundation and practical experience needed to master Large Language Models from the ground up. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Paperback. Condition: new. Paperback. Building a GraphRAG prototype is one thing. Making it fast, reliable, scalable, and production-ready is another.As Retrieval-Augmented Generation evolves beyond basic vector similarity, knowledge graphs are becoming a powerful foundation for AI systems that need to understand relationships, navigate complex information, and reason across connected data. But moving GraphRAG from experimentation into production introduces an entirely new set of challenges: retrieval quality, graph growth, latency, evaluation, cost, security, observability, and scalability.Advanced GraphRAG with Python takes you beyond the fundamentals and into the engineering decisions required to design, optimize, and operate sophisticated GraphRAG systems in real-world environments.Written for developers, AI engineers, machine learning practitioners, and technical architects ready to move past basic implementations, this hands-on guide explores advanced retrieval architectures, graph reasoning strategies, production optimization techniques, and scalable system designs all with practical Python implementations.What You'll LearnDesign advanced GraphRAG architectures for complex and large-scale applications.Engineer scalable knowledge graphs capable of evolving alongside continuously changing data.Combine graph, vector, semantic, and keyword retrieval into powerful hybrid retrieval pipelines.Implement multi-hop retrieval and graph-based reasoning across interconnected information.Apply community detection, hierarchical graph organization, and summarization to improve knowledge discovery.Develop intelligent query planning, routing, and context-selection strategies.Optimize indexing, traversal, caching, and retrieval pipelines for lower latency and higher throughput.Evaluate GraphRAG systems using meaningful retrieval, generation, and end-to-end quality metrics.Reduce hallucinations and improve answer grounding through structured knowledge and evidence-aware retrieval.Build incremental and streaming pipelines that keep knowledge graphs synchronized with changing information.Control LLM, embedding, storage, and infrastructure costs without sacrificing retrieval quality.Design GraphRAG architectures for AI agents, tool-using systems, and multi-step reasoning workflows.Implement monitoring, tracing, logging, and observability for production GraphRAG applications.Address authentication, authorization, privacy, security, governance, and enterprise data access.Deploy resilient GraphRAG services capable of scaling from individual applications to enterprise AI platforms.Go Beyond Building GraphRAG Engineer ItThroughout the book, you'll explore practical architectures and implementation patterns for solving the problems that appear when GraphRAG meets real-world workloads.You'll learn how to make informed engineering trade-offs between retrieval accuracy, latency, cost, complexity, and scalability while developing systems that can be monitored, evaluated, maintained, and continuously improved.Rather than depending entirely on a single framework or vendor, the book emphasizes transferable engineering principles while demonstrating how modern Python tools, graph technologies, retrieval frameworks, vector search, large language models, and production infrastructure can work together.From advanced hybrid retrieval and multi-hop reasoning to distributed processing, agentic workflows, evaluation, optimization, and production deployment, Advanced GraphRAG with Python provides a practical roadmap for taking knowledge graph-powered AI beyond prototypes. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…