Mastering Knowledge Graph Ai Systems - 3 Book Series (18 results)

Taschenbuch. Condition: Neu. Neuware - Most AI systems today rely on unstructured data and approximate retrieval.And that is exactly where they fail.They miss context.They ignore relationships.They produce answers but not understanding.Knowledge Graph Engineering for LLMs is the starting point for building a new class of AI systems systems designed around structured knowledge, connected data, and relationship-aware intelligence.Instead of treating information as isolated text, this book teaches you how to model, connect, and organize data in ways that allow AI systems to understand how everything fits together.You will learn how to design and build knowledge graphs that integrate seamlessly with large language models giving your systems the ability to retrieve accurate context, reduce hallucinations, and generate more reliable outputs.Inside this book, you will learn how to: - Design scalable knowledge graph schemas and ontologies- Extract structured knowledge from unstructured data using modern AI techniques- Build and manage graph-based data systems for real-world applications- Query and traverse relationships to generate meaningful context- Integrate knowledge graphs with LLMs for improved accuracy and reasoning- Combine graph-based retrieval with vector search for hybrid intelligence systems- Design Graph RAG pipelines that go beyond basic retrievalMore importantly, you will understand the engineering decisions behind these systems why they work, when to use them, and how to avoid the mistakes that cause most AI implementations to fail.This is not a theoretical introduction.It is a practical, system-focused guide that builds a solid foundation for advanced AI architectures preparing you for multi-hop reasoning, agentic workflows, and production-scale systems in the next volumes of the series.If you want to move beyond surface-level AI and build systems that are accurate, structured, and context-aware, this is where it begins.Go beyond text.Build systems that understand relationships.…

Taschenbuch. Condition: Neu. Neuware - Building a knowledge graph is only the beginning.The real challenge is making your system think.Most AI applications today rely on basic retrieval pulling fragments of information and hoping the model can make sense of them. But when problems become complex, when context spans multiple relationships, and when accuracy truly matters, these systems begin to fail.They retrieve but they do not reason.Advanced Graph RAG and Reasoning Systems is where AI systems evolve from simple retrieval pipelines into intelligent, relationship-aware architectures capable of multi-step reasoning.This book takes you beyond traditional RAG and introduces a more powerful paradigm one where knowledge is not just retrieved, but connected, traversed, and interpreted across complex structures.Inside this book, you will learn how to: - Design advanced Graph RAG architectures for complex information needs- Implement multi-hop retrieval across deeply connected knowledge graphs- Build reasoning pipelines that follow relationships instead of isolated data points- Combine graph search, vector search, and semantic retrieval into unified systems- Develop LLM-powered agents that interact intelligently with structured knowledge- Orchestrate agentic workflows for research, analysis, and decision-making- Optimize context construction for accuracy, relevance, and efficiency- Evaluate reasoning quality and continuously improve system performanceMore importantly, you will learn how to move from 'query and response' systems to true reasoning systems systems that can navigate relationships, adapt to complexity, and deliver higher-quality, context-aware outputs.This is not theory.This is a deep, engineering-focused guide to building the intelligence layer of modern AI systems where retrieval becomes reasoning, and systems begin to behave like problem solvers rather than search tools.Book 1 gave you the foundation, this book gives you the capability to build AI systems that think in connections, not just text.Go beyond retrieval.Build systems that reason. …

Taschenbuch. Condition: Neu. Neuware - Most AI systems never fail in development.They fail in production.They work in controlled environments but break under real-world pressure.They scale in theory but collapse under load.They generate results but lack reliability, security, and control.Production-Grade Knowledge Graph AI Systems is the final step in building AI systems that don't just work but operate, scale, and survive in real-world environments.This book focuses on what truly separates experimental AI from enterprise-ready systems: engineering discipline, operational excellence, and system reliability.You will learn how to take advanced graph-powered AI architectures and transform them into robust, scalable, and production-ready platforms capable of handling real-world complexity.Inside this book, you will learn how to: - Design distributed architectures for large-scale knowledge graph AI systems- Optimize performance, latency, and cost across multi-stage pipelines- Implement observability, monitoring, and debugging for AI systems in production- Build evaluation frameworks to measure accuracy, reliability, and system quality- Secure graph and LLM integrations against real-world threats- Manage data integrity, governance, and lifecycle at scale- Deploy systems using modern infrastructure and CI/CD pipelines- Continuously improve systems through feedback loops and iterative optimizationMore importantly, you will learn how to think like an engineer responsible for systems that cannot fail systems that must deliver consistent, reliable results under pressure.This book does not focus on theory.It focuses on what breaks, why it breaks, and how to design systems that don't.By the end, you will understand how to move beyond experimentation and build AI systems that are: - Reliable under load- Observable and debuggable- Secure and compliant- Cost-efficient and scalable- Maintainable over timeBook 1 gave you the foundation, and Book 2 gave you intelligence, this book gives you control.The ability to take everything you've built and make it work in the real world.Go beyond prototypes.Build systems that last.…

Paperback. Condition: new. Paperback. Most AI systems today rely on unstructured data and approximate retrieval.And that is exactly where they fail.They miss context.They ignore relationships.They produce answers but not understanding.Knowledge Graph Engineering for LLMs is the starting point for building a new class of AI systems systems designed around structured knowledge, connected data, and relationship-aware intelligence.Instead of treating information as isolated text, this book teaches you how to model, connect, and organize data in ways that allow AI systems to understand how everything fits together.You will learn how to design and build knowledge graphs that integrate seamlessly with large language models giving your systems the ability to retrieve accurate context, reduce hallucinations, and generate more reliable outputs.Inside this book, you will learn how to: Design scalable knowledge graph schemas and ontologiesExtract structured knowledge from unstructured data using modern AI techniquesBuild and manage graph-based data systems for real-world applicationsQuery and traverse relationships to generate meaningful contextIntegrate knowledge graphs with LLMs for improved accuracy and reasoningCombine graph-based retrieval with vector search for hybrid intelligence systemsDesign Graph RAG pipelines that go beyond basic retrievalMore importantly, you will understand the engineering decisions behind these systems why they work, when to use them, and how to avoid the mistakes that cause most AI implementations to fail.This is not a theoretical introduction.It is a practical, system-focused guide that builds a solid foundation for advanced AI architectures preparing you for multi-hop reasoning, agentic workflows, and production-scale systems in the next volumes of the series.If you want to move beyond surface-level AI and build systems that are accurate, structured, and context-aware, this is where it begins.Go beyond text.Build systems that understand relationships. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Building a knowledge graph is only the beginning.The real challenge is making your system think.Most AI applications today rely on basic retrieval pulling fragments of information and hoping the model can make sense of them. But when problems become complex, when context spans multiple relationships, and when accuracy truly matters, these systems begin to fail.They retrieve but they do not reason.Advanced Graph RAG and Reasoning Systems is where AI systems evolve from simple retrieval pipelines into intelligent, relationship-aware architectures capable of multi-step reasoning.This book takes you beyond traditional RAG and introduces a more powerful paradigm one where knowledge is not just retrieved, but connected, traversed, and interpreted across complex structures.Inside this book, you will learn how to: Design advanced Graph RAG architectures for complex information needsImplement multi-hop retrieval across deeply connected knowledge graphsBuild reasoning pipelines that follow relationships instead of isolated data pointsCombine graph search, vector search, and semantic retrieval into unified systemsDevelop LLM-powered agents that interact intelligently with structured knowledgeOrchestrate agentic workflows for research, analysis, and decision-makingOptimize context construction for accuracy, relevance, and efficiencyEvaluate reasoning quality and continuously improve system performanceMore importantly, you will learn how to move from "query and response" systems to true reasoning systems systems that can navigate relationships, adapt to complexity, and deliver higher-quality, context-aware outputs.This is not theory.This is a deep, engineering-focused guide to building the intelligence layer of modern AI systems where retrieval becomes reasoning, and systems begin to behave like problem solvers rather than search tools.Book 1 gave you the foundation, this book gives you the capability to build AI systems that think in connections, not just text.Go beyond retrieval.Build systems that reason. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Most AI systems never fail in development.They fail in production.They work in controlled environments but break under real-world pressure.They scale in theory but collapse under load.They generate results but lack reliability, security, and control.Production-Grade Knowledge Graph AI Systems is the final step in building AI systems that don't just work but operate, scale, and survive in real-world environments.This book focuses on what truly separates experimental AI from enterprise-ready systems: engineering discipline, operational excellence, and system reliability.You will learn how to take advanced graph-powered AI architectures and transform them into robust, scalable, and production-ready platforms capable of handling real-world complexity.Inside this book, you will learn how to: Design distributed architectures for large-scale knowledge graph AI systemsOptimize performance, latency, and cost across multi-stage pipelinesImplement observability, monitoring, and debugging for AI systems in productionBuild evaluation frameworks to measure accuracy, reliability, and system qualitySecure graph and LLM integrations against real-world threatsManage data integrity, governance, and lifecycle at scaleDeploy systems using modern infrastructure and CI/CD pipelinesContinuously improve systems through feedback loops and iterative optimizationMore importantly, you will learn how to think like an engineer responsible for systems that cannot fail systems that must deliver consistent, reliable results under pressure.This book does not focus on theory.It focuses on what breaks, why it breaks, and how to design systems that don't.By the end, you will understand how to move beyond experimentation and build AI systems that are: Reliable under loadObservable and debuggableSecure and compliantCost-efficient and scalableMaintainable over timeBook 1 gave you the foundation, and Book 2 gave you intelligence, this book gives you control.The ability to take everything you've built and make it work in the real world.Go beyond prototypes.Build systems that last. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Most AI systems today rely on unstructured data and approximate retrieval.And that is exactly where they fail.They miss context.They ignore relationships.They produce answers but not understanding.Knowledge Graph Engineering for LLMs is the starting point for building a new class of AI systems systems designed around structured knowledge, connected data, and relationship-aware intelligence.Instead of treating information as isolated text, this book teaches you how to model, connect, and organize data in ways that allow AI systems to understand how everything fits together.You will learn how to design and build knowledge graphs that integrate seamlessly with large language models giving your systems the ability to retrieve accurate context, reduce hallucinations, and generate more reliable outputs.Inside this book, you will learn how to: Design scalable knowledge graph schemas and ontologiesExtract structured knowledge from unstructured data using modern AI techniquesBuild and manage graph-based data systems for real-world applicationsQuery and traverse relationships to generate meaningful contextIntegrate knowledge graphs with LLMs for improved accuracy and reasoningCombine graph-based retrieval with vector search for hybrid intelligence systemsDesign Graph RAG pipelines that go beyond basic retrievalMore importantly, you will understand the engineering decisions behind these systems why they work, when to use them, and how to avoid the mistakes that cause most AI implementations to fail.This is not a theoretical introduction.It is a practical, system-focused guide that builds a solid foundation for advanced AI architectures preparing you for multi-hop reasoning, agentic workflows, and production-scale systems in the next volumes of the series.If you want to move beyond surface-level AI and build systems that are accurate, structured, and context-aware, this is where it begins.Go beyond text.Build systems that understand 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. Building a knowledge graph is only the beginning.The real challenge is making your system think.Most AI applications today rely on basic retrieval pulling fragments of information and hoping the model can make sense of them. But when problems become complex, when context spans multiple relationships, and when accuracy truly matters, these systems begin to fail.They retrieve but they do not reason.Advanced Graph RAG and Reasoning Systems is where AI systems evolve from simple retrieval pipelines into intelligent, relationship-aware architectures capable of multi-step reasoning.This book takes you beyond traditional RAG and introduces a more powerful paradigm one where knowledge is not just retrieved, but connected, traversed, and interpreted across complex structures.Inside this book, you will learn how to: Design advanced Graph RAG architectures for complex information needsImplement multi-hop retrieval across deeply connected knowledge graphsBuild reasoning pipelines that follow relationships instead of isolated data pointsCombine graph search, vector search, and semantic retrieval into unified systemsDevelop LLM-powered agents that interact intelligently with structured knowledgeOrchestrate agentic workflows for research, analysis, and decision-makingOptimize context construction for accuracy, relevance, and efficiencyEvaluate reasoning quality and continuously improve system performanceMore importantly, you will learn how to move from "query and response" systems to true reasoning systems systems that can navigate relationships, adapt to complexity, and deliver higher-quality, context-aware outputs.This is not theory.This is a deep, engineering-focused guide to building the intelligence layer of modern AI systems where retrieval becomes reasoning, and systems begin to behave like problem solvers rather than search tools.Book 1 gave you the foundation, this book gives you the capability to build AI systems that think in connections, not just text.Go beyond retrieval.Build systems that reason. 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. Most AI systems never fail in development.They fail in production.They work in controlled environments but break under real-world pressure.They scale in theory but collapse under load.They generate results but lack reliability, security, and control.Production-Grade Knowledge Graph AI Systems is the final step in building AI systems that don't just work but operate, scale, and survive in real-world environments.This book focuses on what truly separates experimental AI from enterprise-ready systems: engineering discipline, operational excellence, and system reliability.You will learn how to take advanced graph-powered AI architectures and transform them into robust, scalable, and production-ready platforms capable of handling real-world complexity.Inside this book, you will learn how to: Design distributed architectures for large-scale knowledge graph AI systemsOptimize performance, latency, and cost across multi-stage pipelinesImplement observability, monitoring, and debugging for AI systems in productionBuild evaluation frameworks to measure accuracy, reliability, and system qualitySecure graph and LLM integrations against real-world threatsManage data integrity, governance, and lifecycle at scaleDeploy systems using modern infrastructure and CI/CD pipelinesContinuously improve systems through feedback loops and iterative optimizationMore importantly, you will learn how to think like an engineer responsible for systems that cannot fail systems that must deliver consistent, reliable results under pressure.This book does not focus on theory.It focuses on what breaks, why it breaks, and how to design systems that don't.By the end, you will understand how to move beyond experimentation and build AI systems that are: Reliable under loadObservable and debuggableSecure and compliantCost-efficient and scalableMaintainable over timeBook 1 gave you the foundation, and Book 2 gave you intelligence, this book gives you control.The ability to take everything you've built and make it work in the real world.Go beyond prototypes.Build systems that last. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…