Hasting godfrey (35 resultados)

Paperback. Condición: new. Paperback. Build Enterprise-Grade AI Systems with ConfidenceArtificial intelligence has moved beyond experimentation. Today, businesses are building intelligent applications that automate workflows, enhance customer experiences, improve decision-making, and accelerate software development. The real challenge isn't accessing AI-it's building AI systems that are reliable, secure, scalable, and ready for production.Building Enterprise AI with Claude is a comprehensive, hands-on guide designed to help developers, software engineers, and AI professionals build modern AI applications using Claude. Whether you're developing coding agents, Retrieval-Augmented Generation (RAG) systems, Model Context Protocol (MCP) servers, enterprise automation, or intelligent business applications, this book provides the practical knowledge and real-world implementation strategies needed to succeed.Unlike books that focus only on prompt engineering or simple chatbot examples, this guide teaches you how to architect complete AI systems from the ground up. You'll learn industry best practices for designing intelligent applications, integrating external tools, managing enterprise knowledge, deploying scalable solutions, and building AI systems that solve real business problems.Inside This Book, You'll Learn How To: Build production-ready AI applications with Claude.Integrate Claude into web applications, APIs, and enterprise software.Master prompt engineering for accurate, consistent, and structured AI responses.Develop intelligent coding agents capable of reasoning and executing complex tasks.Create enterprise-grade Retrieval-Augmented Generation (RAG) pipelines.Build and integrate Model Context Protocol (MCP) servers.Connect Claude with databases, APIs, cloud platforms, and external tools.Design collaborative multi-agent AI systems.Implement enterprise security, governance, and responsible AI practices.Deploy, monitor, optimize, and scale AI applications in production.Automate business processes with intelligent AI workflows.Complete real-world projects that demonstrate enterprise AI architecture from end to end.Who This Book Is ForSoftware DevelopersPython DevelopersBackend and Full-Stack EngineersAI EngineersSolutions ArchitectsTechnical Leads and Engineering ManagersDevOps and MLOps ProfessionalsStartup Founders and Technical EntrepreneursComputer Science Students and Advanced LearnersWhy This Book Is DifferentThis isn't another book filled with isolated API examples or generic AI theory. Every chapter is designed around practical implementation and production-ready engineering. You'll learn not only how to use Claude, but also how to design intelligent systems that are secure, maintainable, scalable, and capable of supporting real-world business operations.Each concept is reinforced with detailed explanations, architectural guidance, best practices, and hands-on code examples that you can adapt for your own applications. From your first API request to deploying enterprise-scale AI solutions, you'll gain the skills needed to build professional AI systems with confidence.By the End of This Book.You'll have the knowledge and practical experience to design, build, deploy, and scale enterprise AI applications powered by Claude. You'll be able to create intelligent assistants, coding agents, RAG systems, MCP integrations, AI automation workflows, and production-ready solutions that deliver real value to organizations.If you're ready to move beyond basic AI demos and build enterprise-grade applications that solve real problems, Building Enterprise A Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. What separates a basic AI application from a production-grade intelligent system?The answer is rarely the model alone. It is the retrieval infrastructure behind it.As large language models continue to reshape software, one challenge remains constant: getting AI systems to access the right information at the right time, at scale. Whether you are building Retrieval-Augmented Generation (RAG) pipelines, autonomous AI agents, semantic search engines, or long-term memory systems, the quality of your vector search architecture directly determines performance, reliability, and user trust.Claude-Powered Vector Search is a practical, engineering-focused guide designed to help you master the infrastructure powering modern AI retrieval systems.This book goes far beyond surface-level explanations. Instead of treating vector databases as black boxes, it teaches you how vector search works from first principles and how to design, deploy, optimize, and scale production-ready retrieval pipelines using modern tools and proven engineering practices.You will learn how embeddings represent semantic meaning, how similarity search works in high-dimensional spaces, why approximate nearest neighbor algorithms matter, and how production vector databases handle billions of embeddings with low-latency retrieval. More importantly, you will understand how all these components work together to power real AI applications.From foundational mathematics to enterprise-scale architecture, this book bridges theory and implementation with detailed explanations, practical examples, and production-aware design strategies.Inside this book, you will learn how to: Build vector search systems from scratch using Python and understand the mechanics behind similarity search and indexing.Engineer high-quality embeddings optimized for domain-specific retrieval tasks.Compare leading vector databases such as Pinecone, Qdrant, Weaviate, and FAISS to choose the right solution for your workload.Design high-performance Retrieval-Augmented Generation pipelines for AI systems powered by Claude.Optimize query pipelines with hybrid search, re-ranking, and relevance tuning techniques.Scale retrieval infrastructure to billions of vectors using sharding, replication, GPU acceleration, and distributed search.Build persistent memory architectures that enable AI agents to maintain long-term contextual intelligence.Monitor, evaluate, and tune production retrieval systems for accuracy, recall, precision, latency, and reliability.Understand emerging architectures such as multimodal retrieval, agentic search, and memory-centric AI infrastructure.Who This Book Is ForThis book is ideal for: AI EngineersMachine Learning EngineersBackend and Infrastructure EngineersData ScientistsSoftware ArchitectsAdvanced Developers and Technical FoundersIf you have ever struggled with hallucinations in LLM applications, poor retrieval quality, expensive inference pipelines, or scaling vector databases under real workloads, this book was written for you.This is not a theoretical overview.This is an engineering manual for serious builders.By the end of this book, you will have a deep understanding of how modern vector search systems work under the hood and how to build infrastructure capable of supporting next-generation AI products.The future of intelligent software will not be powered by models alone.It will be powered by systems that can retrieve, rank, reason over, and persist knowledge efficiently.Claude-Powered Vector Search gives you the technical depth and practical expertise to build those syst Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Most inventory problems do not start in the warehouse.They start in decisions.A delayed purchase order.A weak forecast.A missed demand signal.A supplier delay nobody saw coming.These small failures can quietly grow into stockouts, excess inventory, wasted capital, operational bottlenecks, and lost revenue.In today's fast-moving business environment, traditional inventory management is no longer enough.Spreadsheets, static reorder points, manual forecasting, and reactive planning cannot keep up with volatile demand, global supply chain disruptions, rising customer expectations, and increasingly complex operations.This is where artificial intelligence changes everything.AI-Powered Inventory Manager is a practical, business-focused guide that shows inventory professionals how to use artificial intelligence to make smarter inventory decisions, reduce waste, improve forecasting accuracy, and build resilient supply chains.This book goes beyond theory.It translates AI into real operational value.Whether you manage inventory in retail, e-commerce, manufacturing, pharmaceuticals, FMCG, logistics, or wholesale distribution, this book shows you how modern AI systems can transform daily inventory operations and long-term supply chain strategy.Inside this book, you will learn how to: Use AI-driven demand forecasting to predict sales more accurately and reduce costly planning errors.Optimize stock levels using intelligent models that balance service levels, safety stock, and working capital efficiency.Detect slow-moving inventory before it becomes dead stock and expensive waste.Prevent stockouts by identifying supply risks, demand spikes, and replenishment failures early.Improve warehouse operations through AI-powered visibility, automation, and operational intelligence.Evaluate supplier performance using predictive analytics and smarter procurement decision-making.Integrate AI tools with enterprise systems such as ERP, business intelligence platforms, and inventory management software.Measure ROI and prove the business value of AI-driven inventory transformation.This book is designed for professionals who want practical results, not buzzwords.This book is for: Inventory Managers who want stronger control over stock levels, replenishment, and service performance.Warehouse Leaders seeking improved operational efficiency, inventory visibility, and smarter storage strategies.Procurement and Supply Chain Professionals looking to make better sourcing and supplier decisions using data-driven intelligence.Operations Managers and Business Leaders responsible for reducing cost, improving service quality, and increasing operational resilience.Even if you have no technical background in artificial intelligence or machine learning, this book explains complex concepts in clear business language with practical examples you can apply immediately.You do not need to be a data scientist.That is exactly what this book teaches.Artificial intelligence is rapidly changing how businesses plan, buy, store, move, and replenish inventory. Companies that adapt early will gain a major competitive advantage through lower costs, faster decisions, better service levels, and stronger resilience during disruption.AI-Powered Inventory Manager gives you the knowledge, frameworks, and practical strategies to lead that transformation.If you want to reduce waste, improve profitability, strengthen supply chain performance, and become a smarter inventory decision-maker, this book will become an essential resource in your professional library.The future of inve Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Build Enterprise-Grade AI Systems with ConfidenceArtificial intelligence has moved beyond experimentation. Today, businesses are building intelligent applications that automate workflows, enhance customer experiences, improve decision-making, and accelerate software development. The real challenge isn't accessing AI-it's building AI systems that are reliable, secure, scalable, and ready for production.Building Enterprise AI with Claude is a comprehensive, hands-on guide designed to help developers, software engineers, and AI professionals build modern AI applications using Claude. Whether you're developing coding agents, Retrieval-Augmented Generation (RAG) systems, Model Context Protocol (MCP) servers, enterprise automation, or intelligent business applications, this book provides the practical knowledge and real-world implementation strategies needed to succeed.Unlike books that focus only on prompt engineering or simple chatbot examples, this guide teaches you how to architect complete AI systems from the ground up. You'll learn industry best practices for designing intelligent applications, integrating external tools, managing enterprise knowledge, deploying scalable solutions, and building AI systems that solve real business problems.Inside This Book, You'll Learn How To: Build production-ready AI applications with Claude.Integrate Claude into web applications, APIs, and enterprise software.Master prompt engineering for accurate, consistent, and structured AI responses.Develop intelligent coding agents capable of reasoning and executing complex tasks.Create enterprise-grade Retrieval-Augmented Generation (RAG) pipelines.Build and integrate Model Context Protocol (MCP) servers.Connect Claude with databases, APIs, cloud platforms, and external tools.Design collaborative multi-agent AI systems.Implement enterprise security, governance, and responsible AI practices.Deploy, monitor, optimize, and scale AI applications in production.Automate business processes with intelligent AI workflows.Complete real-world projects that demonstrate enterprise AI architecture from end to end.Who This Book Is ForSoftware DevelopersPython DevelopersBackend and Full-Stack EngineersAI EngineersSolutions ArchitectsTechnical Leads and Engineering ManagersDevOps and MLOps ProfessionalsStartup Founders and Technical EntrepreneursComputer Science Students and Advanced LearnersWhy This Book Is DifferentThis isn't another book filled with isolated API examples or generic AI theory. Every chapter is designed around practical implementation and production-ready engineering. You'll learn not only how to use Claude, but also how to design intelligent systems that are secure, maintainable, scalable, and capable of supporting real-world business operations.Each concept is reinforced with detailed explanations, architectural guidance, best practices, and hands-on code examples that you can adapt for your own applications. From your first API request to deploying enterprise-scale AI solutions, you'll gain the skills needed to build professional AI systems with confidence.By the End of This Book.You'll have the knowledge and practical experience to design, build, deploy, and scale enterprise AI applications powered by Claude. You'll be able to create intelligent assistants, coding agents, RAG systems, MCP integrations, AI automation workflows, and production-ready solutions that deliver real value to organizations.If you're ready to move beyond basic AI demos and build enterprise-grade applications that solve real problems, Building Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware - A practical, engineering-focused guide to modern AI search systems, designed to help you understand how intelligent search really works beyond prompts and chat interfaces.About This BookAdvanced Perplexity AI is not a surface-level introduction to AI tools. It is a systems-level breakdown of how modern AI search engines are built, how they retrieve information, and how they generate accurate, source-backed responses.If you have ever used AI search tools like Perplexity and wondered how they consistently return structured answers with citations, this book explains exactly what is happening under the hood in a clear and practical way.Who This Book Is For- Developers building AI-powered applications- AI engineers working with LLMs and RAG systems- Data scientists exploring retrieval-based intelligence- Technical researchers and system designers- Professionals building internal search or knowledge systemsWhat You Will GainBy the end of this book, you will understand how to design and think about AI search systems at a production level.- How AI search systems retrieve and rank information- How hallucinations are reduced using structured grounding- How RAG (Retrieval-Augmented Generation) systems are built- How autonomous research agents operate- How memory and context systems are designed- How citations and source validation actually work- How enterprise AI search systems are structuredWhy This Book MattersAI search is becoming a core infrastructure layer for modern applications. It is no longer just about finding information, but about structuring intelligence, improving decision-making, and powering real-time knowledge systems.This book gives you the engineering clarity needed to move from using AI tools to building them. You will understand not just what these systems do, but how they are designed and deployed in real environments.If you want to build real AI systems, understand retrieval pipelines, or design production-ready AI search applications, this book gives you the foundation to do it.…

Paperback. Condición: new. Paperback. What separates a basic AI application from a production-grade intelligent system?The answer is rarely the model alone. It is the retrieval infrastructure behind it.As large language models continue to reshape software, one challenge remains constant: getting AI systems to access the right information at the right time, at scale. Whether you are building Retrieval-Augmented Generation (RAG) pipelines, autonomous AI agents, semantic search engines, or long-term memory systems, the quality of your vector search architecture directly determines performance, reliability, and user trust.Claude-Powered Vector Search is a practical, engineering-focused guide designed to help you master the infrastructure powering modern AI retrieval systems.This book goes far beyond surface-level explanations. Instead of treating vector databases as black boxes, it teaches you how vector search works from first principles and how to design, deploy, optimize, and scale production-ready retrieval pipelines using modern tools and proven engineering practices.You will learn how embeddings represent semantic meaning, how similarity search works in high-dimensional spaces, why approximate nearest neighbor algorithms matter, and how production vector databases handle billions of embeddings with low-latency retrieval. More importantly, you will understand how all these components work together to power real AI applications.From foundational mathematics to enterprise-scale architecture, this book bridges theory and implementation with detailed explanations, practical examples, and production-aware design strategies.Inside this book, you will learn how to: Build vector search systems from scratch using Python and understand the mechanics behind similarity search and indexing.Engineer high-quality embeddings optimized for domain-specific retrieval tasks.Compare leading vector databases such as Pinecone, Qdrant, Weaviate, and FAISS to choose the right solution for your workload.Design high-performance Retrieval-Augmented Generation pipelines for AI systems powered by Claude.Optimize query pipelines with hybrid search, re-ranking, and relevance tuning techniques.Scale retrieval infrastructure to billions of vectors using sharding, replication, GPU acceleration, and distributed search.Build persistent memory architectures that enable AI agents to maintain long-term contextual intelligence.Monitor, evaluate, and tune production retrieval systems for accuracy, recall, precision, latency, and reliability.Understand emerging architectures such as multimodal retrieval, agentic search, and memory-centric AI infrastructure.Who This Book Is ForThis book is ideal for: AI EngineersMachine Learning EngineersBackend and Infrastructure EngineersData ScientistsSoftware ArchitectsAdvanced Developers and Technical FoundersIf you have ever struggled with hallucinations in LLM applications, poor retrieval quality, expensive inference pipelines, or scaling vector databases under real workloads, this book was written for you.This is not a theoretical overview.This is an engineering manual for serious builders.By the end of this book, you will have a deep understanding of how modern vector search systems work under the hood and how to build infrastructure capable of supporting next-generation AI products.The future of intelligent software will not be powered by models alone.It will be powered by systems that can retrieve, rank, reason over, and persist knowledge efficiently.Claude-Powered Vector Search gives you the technical depth and practical expertise to bui Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Most inventory problems do not start in the warehouse.They start in decisions.A delayed purchase order.A weak forecast.A missed demand signal.A supplier delay nobody saw coming.These small failures can quietly grow into stockouts, excess inventory, wasted capital, operational bottlenecks, and lost revenue.In today's fast-moving business environment, traditional inventory management is no longer enough.Spreadsheets, static reorder points, manual forecasting, and reactive planning cannot keep up with volatile demand, global supply chain disruptions, rising customer expectations, and increasingly complex operations.This is where artificial intelligence changes everything.AI-Powered Inventory Manager is a practical, business-focused guide that shows inventory professionals how to use artificial intelligence to make smarter inventory decisions, reduce waste, improve forecasting accuracy, and build resilient supply chains.This book goes beyond theory.It translates AI into real operational value.Whether you manage inventory in retail, e-commerce, manufacturing, pharmaceuticals, FMCG, logistics, or wholesale distribution, this book shows you how modern AI systems can transform daily inventory operations and long-term supply chain strategy.Inside this book, you will learn how to: Use AI-driven demand forecasting to predict sales more accurately and reduce costly planning errors.Optimize stock levels using intelligent models that balance service levels, safety stock, and working capital efficiency.Detect slow-moving inventory before it becomes dead stock and expensive waste.Prevent stockouts by identifying supply risks, demand spikes, and replenishment failures early.Improve warehouse operations through AI-powered visibility, automation, and operational intelligence.Evaluate supplier performance using predictive analytics and smarter procurement decision-making.Integrate AI tools with enterprise systems such as ERP, business intelligence platforms, and inventory management software.Measure ROI and prove the business value of AI-driven inventory transformation.This book is designed for professionals who want practical results, not buzzwords.This book is for: Inventory Managers who want stronger control over stock levels, replenishment, and service performance.Warehouse Leaders seeking improved operational efficiency, inventory visibility, and smarter storage strategies.Procurement and Supply Chain Professionals looking to make better sourcing and supplier decisions using data-driven intelligence.Operations Managers and Business Leaders responsible for reducing cost, improving service quality, and increasing operational resilience.Even if you have no technical background in artificial intelligence or machine learning, this book explains complex concepts in clear business language with practical examples you can apply immediately.You do not need to be a data scientist.That is exactly what this book teaches.Artificial intelligence is rapidly changing how businesses plan, buy, store, move, and replenish inventory. Companies that adapt early will gain a major competitive advantage through lower costs, faster decisions, better service levels, and stronger resilience during disruption.AI-Powered Inventory Manager gives you the knowledge, frameworks, and practical strategies to lead that transformation.If you want to reduce waste, improve profitability, strengthen supply chain performance, and become a smarter inventory decision-maker, this book will become an essential resource in your professional library.The f Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. A practical, engineering-focused guide to modern AI search systems, designed to help you understand how intelligent search really works beyond prompts and chat interfaces.About This BookAdvanced Perplexity AI is not a surface-level introduction to AI tools. It is a systems-level breakdown of how modern AI search engines are built, how they retrieve information, and how they generate accurate, source-backed responses.If you have ever used AI search tools like Perplexity and wondered how they consistently return structured answers with citations, this book explains exactly what is happening under the hood in a clear and practical way.Who This Book Is ForDevelopers building AI-powered applicationsAI engineers working with LLMs and RAG systemsData scientists exploring retrieval-based intelligenceTechnical researchers and system designersProfessionals building internal search or knowledge systemsWhat You Will GainBy the end of this book, you will understand how to design and think about AI search systems at a production level.How AI search systems retrieve and rank informationHow hallucinations are reduced using structured groundingHow RAG (Retrieval-Augmented Generation) systems are builtHow autonomous research agents operateHow memory and context systems are designedHow citations and source validation actually workHow enterprise AI search systems are structuredWhy This Book MattersAI search is becoming a core infrastructure layer for modern applications. It is no longer just about finding information, but about structuring intelligence, improving decision-making, and powering real-time knowledge systems.This book gives you the engineering clarity needed to move from using AI tools to building them. You will understand not just what these systems do, but how they are designed and deployed in real environments.If you want to build real AI systems, understand retrieval pipelines, or design production-ready AI search applications, this book gives you the foundation to do it. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. Build Firmware That Works Beyond the LabA blinking LED is easy. Building firmware that powers reliable commercial products is a different challenge altogether.Embedded Firmware Engineering is a practical, hands-on guide that takes you beyond basic microcontroller programming and teaches you how professional firmware is designed, tested, optimized, and prepared for production. Whether you're developing your first embedded application or building sophisticated real-time systems, this book provides the knowledge and engineering practices used to create dependable embedded products.Instead of focusing only on programming syntax or hardware theory, this book combines both disciplines to show how firmware and hardware work together in real-world products. You'll learn how to write clean Embedded C code, design scalable firmware architectures, develop reliable device drivers, build real-time applications with an RTOS, optimize system performance, and confidently transition from prototype to production hardware.Inside this book, you'll learn how to: Master Embedded C programming for professional firmware development.Understand microcontroller architecture, memory organization, interrupts, and peripherals.Work with UART, SPI, I2C, CAN, timers, GPIO, and other hardware interfaces.Design modular firmware architectures that are scalable and easy to maintain.Develop multitasking applications using Real-Time Operating Systems (RTOS).Create reusable device drivers for sensors, displays, motors, and communication modules.Optimize memory usage, execution speed, and power consumption.Apply professional debugging and testing techniques to identify and resolve firmware issues.Build secure, reliable, and production-ready embedded systems.Prepare firmware for connected devices, IoT applications, and OTA updates.Transition successfully from development boards to commercial hardware.Follow industry best practices used by professional embedded firmware engineers.Who this book is forEmbedded firmware engineers.Electrical and electronics engineers.Computer engineering students.Embedded systems developers.IoT and robotics developers.Makers and advanced electronics hobbyists.Software developers transitioning into embedded systems.Every chapter builds on practical engineering principles and includes real firmware examples where they add the greatest value. Rather than overwhelming you with unnecessary theory, the book emphasizes techniques and workflows that you can immediately apply to your own embedded projects.By the time you finish this book, you'll understand how commercial firmware is designed, tested, optimized, maintained, and deployed throughout the entire product lifecycle. More importantly, you'll have the confidence to engineer embedded systems that are reliable, scalable, efficient, and ready for deployment in industrial, automotive, robotics, consumer electronics, and Internet of Things (IoT) applications.If you're ready to move beyond simple microcontroller projects and build firmware like a professional engineer, Embedded Firmware Engineering is your complete guide. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Modern AI systems are only as powerful as their ability to retrieve the right information at the right time. As applications move beyond simple chatbots into semantic search engines, recommendation systems, RAG pipelines, and autonomous AI agents, vector databases have become the core infrastructure behind intelligent retrieval.Production Vector Databases is a practical, engineering-focused guide to building high-performance similarity search and retrieval systems that work at scale. This book goes far beyond theory. It breaks down how real production systems are designed, optimized, deployed, and maintained using tools like FAISS, Milvus, Pinecone, Weaviate, and modern orchestration frameworks.Inside, you will learn how to design and implement vector-based architectures that power real AI applications, from embedding pipelines to distributed search systems and cloud-native deployments. Every concept is explained with production-level clarity and supported with practical code examples that reflect real engineering environments.This book is written for engineers who want to move from understanding vector search to building systems that can handle real-world traffic, real data volumes, and real performance constraints.It is especially useful for: AI engineers building retrieval-augmented generation (RAG) systems and agent memory layersMachine learning engineers working on semantic search, recommendation engines, and embedding pipelinesBackend engineers transitioning into AI infrastructure and distributed systemsData engineers responsible for large-scale indexing, storage, and retrieval pipelinesTechnical founders and builders creating AI-powered products and SaaS platformsAdvanced learners who want to understand how production vector databases actually work under the hoodThe book walks through the full lifecycle of a retrieval system. It starts from embeddings and similarity search fundamentals, then moves into indexing strategies, approximate nearest neighbor algorithms, and scalable vector storage architectures. From there, it progresses into production topics such as distributed search, replication, fault tolerance, caching, observability, security, and cost optimization.You will also learn how to design complete AI retrieval platforms using modern infrastructure tools, including Docker, Kubernetes, and cloud services. The focus is not just on building systems that work, but systems that are stable, efficient, and ready for production deployment.Unlike introductory materials, this book focuses on engineering decisions that matter in real systems: how to balance speed and accuracy, how to reduce infrastructure costs at scale, how to maintain recall under heavy optimization, and how to design architectures that remain flexible as models and workloads evolve.By the end of this book, you will understand how large-scale vector retrieval systems are built and how to design your own production-ready AI infrastructure from scratch.If you are serious about building scalable AI systems that go beyond prototypes and into real-world production, this book gives you the architectural thinking, implementation detail, and engineering depth required to get there. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Build Faster, Smarter, and Production-Ready AI Systems with ClaudeArtificial intelligence is no longer defined by building powerful models alone. The real challenge is deploying them efficiently, serving millions of requests reliably, controlling infrastructure costs, and delivering fast, dependable experiences in production. That is where inference engineering becomes one of the most valuable skills in modern AI.Production AI Inference Engineering with Claude is a comprehensive, hands-on guide that takes you beyond theory and into the engineering practices used to build scalable AI systems. Whether you're deploying Claude-powered applications, optimizing open-weight models, or designing hybrid AI infrastructure, this book provides the knowledge and practical techniques needed to build production-ready solutions with confidence.Rather than focusing solely on APIs or isolated code examples, this book explores the complete inference lifecycle-from understanding how transformer inference works to optimizing latency, reducing GPU memory usage, implementing KV cache strategies, deploying high-throughput inference servers, and designing enterprise-grade AI architectures that remain reliable as demand grows.You'll learn how Claude fits into modern AI infrastructure and how to integrate it with today's most powerful inference technologies. Along the way, you'll gain practical insight into how experienced AI engineers solve real production challenges, make architectural decisions, measure performance, and balance speed, scalability, security, and operational cost.What You'll LearnBuild production-ready AI applications using Claude.Understand the complete LLM inference pipeline from tokenization to response generation.Optimize inference with FP16, BF16, INT8, GPTQ, AWQ, and other modern quantization techniques.Improve latency and throughput using KV cache optimization, prompt caching, continuous batching, and speculative decoding.Create reliable request pipelines with streaming, structured outputs, and tool use.Deploy high-performance inference servers using vLLM, TensorRT-LLM, and modern serving frameworks.Build hybrid AI systems that combine Claude with open-weight language models.Scale AI workloads across GPUs, containers, and cloud infrastructure.Monitor, secure, and optimize production AI services.Design AI platforms that remain adaptable as models, hardware, and infrastructure continue to evolve.Who This Book Is ForMachine Learning Engineers optimizing inference performance and deployment.AI Engineers building Claude-powered applications and enterprise AI systems.Backend Developers expanding into modern AI infrastructure.MLOps and Platform Engineers responsible for deploying and scaling large language models.Software Engineers who want to understand what happens after model training.Solutions Architects designing scalable, secure, and cost-efficient AI platforms.Technical professionals looking to transition from AI experimentation to production deployment.Take Your AI Engineering Skills to the Next LevelWhether you're developing AI assistants, coding tools, enterprise search platforms, customer support systems, document intelligence solutions, or intelligent automation workflows, the techniques in this book will help you build systems that are faster, more scalable, more reliable, and easier to maintain.The future of AI belongs not only to those who build intelligent models, but to the engineers who know how to deploy, optimize, and scale them. Start building that expertise today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. Artificial intelligence is no longer limited to chatbots responding to prompts. The future belongs to systems that can remember, retrieve, reason, and act-AI agents powered by robust knowledge infrastructure.But here's the challenge: most AI applications fail not because the model is weak, but because the knowledge system behind it is poorly designed.Production AI Knowledge Systems is a practical, engineering-focused guide that teaches you how to build the hidden infrastructure that makes modern AI systems truly intelligent. Instead of relying on fragile prompts and short context windows, this book shows you how to architect scalable knowledge systems that AI agents can actually understand, search, and use in production.Whether you are building retrieval-augmented generation (RAG) pipelines, AI copilots, autonomous agents, enterprise search systems, or context-aware applications, this book gives you the tools, architecture patterns, and implementation strategies needed to move from experimentation to production-grade deployment.Inside this book, you will learn how to: Design LLM-readable documentation that improves reasoning and reduces hallucinationBuild structured HTML artifacts and AI-native knowledge bases optimized for machine consumptionCreate scalable LLM wikis for organizing large bodies of technical knowledgeImplement efficient chunking strategies for better semantic retrievalGenerate and optimize vector embeddings for high-quality similarity searchBuild modern retrieval pipelines using dense, sparse, and hybrid search techniquesUnderstand vector indexing methods and choose the right vector database architectureEngineer memory systems for AI agents with long-term contextual awarenessDesign multi-agent knowledge orchestration systems for collaborative AI workflowsDeploy and optimize AI infrastructure for latency, scalability, cost, and reliabilityUnlike theory-heavy books that stop at concepts, this guide emphasizes real implementation. You will explore production-ready code examples, architectural diagrams, engineering trade-offs, and practical deployment strategies used in modern AI systems.This book is for: AI Engineers building production LLM applicationsMachine Learning Engineers working on retrieval systemsSoftware Engineers integrating AI into productsRAG Developers designing search and knowledge pipelinesTechnical Architects building enterprise AI infrastructureAdvanced learners who want to understand how modern AI systems work under the hoodIf you have ever wondered how advanced AI assistants maintain context, retrieve the right information at the right time, and deliver reliable responses across complex workflows, this book provides the answer.The era of prompt engineering alone is ending.The next generation of intelligent systems will be built on knowledge engineering, context architecture, and retrieval infrastructure.This book will show you how to build them. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Artificial Intelligence is rapidly reshaping the way software is built, deployed, and scaled. From intelligent assistants and coding agents to autonomous workflows and enterprise automation, AI is no longer a futuristic concept-it is becoming a core part of modern software engineering.Yet for many developers, there is still a major challenge.Learning about AI is easy. Building AI systems that are reliable, scalable, secure, and production-ready is where the real complexity begins.Building Systems with Claude System is a practical, hands-on guide created to help developers bridge that gap. Rather than focusing on theory or high-level concepts alone, this book teaches you how to design and build real AI systems powered by Claude for practical applications.Whether you want to build AI-powered software, intelligent coding assistants, document-aware systems, retrieval pipelines, or production automation workflows, this book provides the technical foundation and implementation strategies to help you do it with confidence.Inside this book, you will learn how modern AI systems are architected from end to end. You will explore how large language models fit into production applications, how to integrate Claude into software workflows, and how to engineer systems that can reason, retrieve knowledge, use tools, and perform autonomous tasks.You will gain practical experience building AI applications, coding agents, and Retrieval-Augmented Generation (RAG) pipelines that can search, understand, and respond using external knowledge sources. You will also learn how to design multi-step AI workflows that move beyond simple prompting into intelligent system orchestration.More importantly, this book goes beyond building prototypes.You will learn what it takes to deploy AI systems in real-world environments, including performance optimization, observability, security, governance, cost management, and reliability engineering. These are the practical concerns that separate experimental projects from production-grade AI systems.Each chapter combines clear explanations with practical code examples and implementation-focused guidance, making complex topics easier to understand and apply in real development scenarios.This book is ideal for: Software developers building AI-powered applicationsBackend engineers designing production AI architecturesAI engineers and machine learning practitionersTechnical founders developing AI productsAutomation specialists building intelligent workflowsPython developers seeking practical AI implementation skillsBy the end of this book, you will know how to build intelligent systems that can power modern applications, automate workflows, enhance developer productivity, and solve real business problems.If you are ready to move beyond AI experimentation and start building scalable, production-ready AI systems with confidence, this book will serve as your practical roadmap.The future of software is intelligent, adaptive, and increasingly autonomous.Start building it today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Artificial intelligence is no longer limited to chatbots responding to prompts. The future belongs to systems that can remember, retrieve, reason, and act-AI agents powered by robust knowledge infrastructure.But here's the challenge: most AI applications fail not because the model is weak, but because the knowledge system behind it is poorly designed.Production AI Knowledge Systems is a practical, engineering-focused guide that teaches you how to build the hidden infrastructure that makes modern AI systems truly intelligent. Instead of relying on fragile prompts and short context windows, this book shows you how to architect scalable knowledge systems that AI agents can actually understand, search, and use in production.Whether you are building retrieval-augmented generation (RAG) pipelines, AI copilots, autonomous agents, enterprise search systems, or context-aware applications, this book gives you the tools, architecture patterns, and implementation strategies needed to move from experimentation to production-grade deployment.Inside this book, you will learn how to: Design LLM-readable documentation that improves reasoning and reduces hallucinationBuild structured HTML artifacts and AI-native knowledge bases optimized for machine consumptionCreate scalable LLM wikis for organizing large bodies of technical knowledgeImplement efficient chunking strategies for better semantic retrievalGenerate and optimize vector embeddings for high-quality similarity searchBuild modern retrieval pipelines using dense, sparse, and hybrid search techniquesUnderstand vector indexing methods and choose the right vector database architectureEngineer memory systems for AI agents with long-term contextual awarenessDesign multi-agent knowledge orchestration systems for collaborative AI workflowsDeploy and optimize AI infrastructure for latency, scalability, cost, and reliabilityUnlike theory-heavy books that stop at concepts, this guide emphasizes real implementation. You will explore production-ready code examples, architectural diagrams, engineering trade-offs, and practical deployment strategies used in modern AI systems.This book is for: AI Engineers building production LLM applicationsMachine Learning Engineers working on retrieval systemsSoftware Engineers integrating AI into productsRAG Developers designing search and knowledge pipelinesTechnical Architects building enterprise AI infrastructureAdvanced learners who want to understand how modern AI systems work under the hoodIf you have ever wondered how advanced AI assistants maintain context, retrieve the right information at the right time, and deliver reliable responses across complex workflows, this book provides the answer.The era of prompt engineering alone is ending.The next generation of intelligent systems will be built on knowledge engineering, context architecture, and retrieval infrastructure.This book will show you how to build them. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…