Alex mercerfield (12 resultados)

Paperback. Condición: new. Paperback. Stop Building AI Demos. Start Building Production AI Agents.Anyone can connect an LLM to an API and call it an AI agent. Very few engineers know how to build Production AI Agents that are reliable, scalable, secure, observable, and ready for enterprise deployment.If you've struggled with hallucinations, unreliable outputs, poor tool integration, limited context, deployment complexity, or scaling AI beyond proof-of-concept projects, you're not alone. Most AI books focus on prompts, isolated frameworks, or simple demos-but few teach the AI Agent Engineering principles required to build intelligent systems organizations can trust.This book bridges that gap.Building AI Agents for Production Systems is a practical, engineering-first guide to designing, deploying, and operating modern Enterprise AI applications. Instead of focusing on a single framework, it teaches the production architecture, design patterns, and engineering practices that remain valuable as AI technologies evolve.From architecture to deployment, you'll build intelligent applications using MCP (Model Context Protocol), Memory Engineering, RAG Engineering, Tool Calling, AI Orchestration, Multi-Agent Systems, observability, security, governance, and enterprise integration through four complete end-to-end production projects.Inside this book, you'll learn how to: Build Production AI Agents instead of simple chatbotsMaster the foundations of AI Agent EngineeringDesign scalable AI System Design architecturesEngineer reliable single-agent and Multi-Agent SystemsImplement MCP (Model Context Protocol) for enterprise integrationBuild persistent memory and advanced context managementApply RAG Engineering to reduce hallucinations and improve accuracyCreate secure Tool Calling workflows and enterprise API integrationsDevelop robust AI Orchestration for autonomous agentsDeploy AI systems using Docker, cloud platforms, and CI/CDMonitor, evaluate, trace, and optimize production AI systemsApply enterprise-grade security, governance, and guardrailsBuild four complete production-ready AI applicationsBuild Real-World ProjectsEnterprise Knowledge AssistantAutonomous Customer Support PlatformMulti-Agent Business Workflow AutomationIntelligent Operations Monitoring PlatformPerfect for: AI EngineersSoftware EngineersBackend EngineersLLM Engineering professionalsMachine Learning EngineersPlatform & DevOps EngineersSolution ArchitectsEnterprise DevelopersTechnical LeadsAnyone building modern Agentic AI solutionsUnlike books centered on a single library or rapidly changing implementation details, this guide teaches timeless engineering principles that apply across today's leading AI frameworks and tomorrow's technologies. You'll learn how to design intelligent systems that are maintainable, scalable, observable, secure, and production-ready.Whether you're building enterprise assistants, autonomous workflows, developer tools, internal copilots, or large-scale Enterprise AI platforms, this book provides a complete roadmap from concept to production.The future belongs to engineers who can build reliable Production AI Agents-not just AI demos. If you're ready to master AI Agent Engineering, build intelligent Multi-Agent Systems, leverage MCP, RAG Engineering, and AI Orchestration, and deploy production-ready Enterprise AI applications with confidence, this book is your blueprint Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condición: Neu. Neuware.

Paperback. Condición: new. Paperback. Stop Building AI Demos. Start Building Production AI Agents.Anyone can connect an LLM to an API and call it an AI agent. Very few engineers know how to build Production AI Agents that are reliable, scalable, secure, observable, and ready for enterprise deployment.If you've struggled with hallucinations, unreliable outputs, poor tool integration, limited context, deployment complexity, or scaling AI beyond proof-of-concept projects, you're not alone. Most AI books focus on prompts, isolated frameworks, or simple demos-but few teach the AI Agent Engineering principles required to build intelligent systems organizations can trust.This book bridges that gap.Building AI Agents for Production Systems is a practical, engineering-first guide to designing, deploying, and operating modern Enterprise AI applications. Instead of focusing on a single framework, it teaches the production architecture, design patterns, and engineering practices that remain valuable as AI technologies evolve.From architecture to deployment, you'll build intelligent applications using MCP (Model Context Protocol), Memory Engineering, RAG Engineering, Tool Calling, AI Orchestration, Multi-Agent Systems, observability, security, governance, and enterprise integration through four complete end-to-end production projects.Inside this book, you'll learn how to: Build Production AI Agents instead of simple chatbotsMaster the foundations of AI Agent EngineeringDesign scalable AI System Design architecturesEngineer reliable single-agent and Multi-Agent SystemsImplement MCP (Model Context Protocol) for enterprise integrationBuild persistent memory and advanced context managementApply RAG Engineering to reduce hallucinations and improve accuracyCreate secure Tool Calling workflows and enterprise API integrationsDevelop robust AI Orchestration for autonomous agentsDeploy AI systems using Docker, cloud platforms, and CI/CDMonitor, evaluate, trace, and optimize production AI systemsApply enterprise-grade security, governance, and guardrailsBuild four complete production-ready AI applicationsBuild Real-World ProjectsEnterprise Knowledge AssistantAutonomous Customer Support PlatformMulti-Agent Business Workflow AutomationIntelligent Operations Monitoring PlatformPerfect for: AI EngineersSoftware EngineersBackend EngineersLLM Engineering professionalsMachine Learning EngineersPlatform & DevOps EngineersSolution ArchitectsEnterprise DevelopersTechnical LeadsAnyone building modern Agentic AI solutionsUnlike books centered on a single library or rapidly changing implementation details, this guide teaches timeless engineering principles that apply across today's leading AI frameworks and tomorrow's technologies. You'll learn how to design intelligent systems that are maintainable, scalable, observable, secure, and production-ready.Whether you're building enterprise assistants, autonomous workflows, developer tools, internal copilots, or large-scale Enterprise AI platforms, this book provides a complete roadmap from concept to production.The future belongs to engineers who can build reliable Production AI Agents-not just AI demos. If you're ready to master AI Agent Engineering, build intelligent Multi-Agent Systems, leverage MCP, RAG Engineering, and AI Orchestration, and deploy production-ready Enterprise AI applications with confidence, this book is yo Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. Neuware.

Paperback. Condición: new. Paperback. Your Data Is Growing. But Is Your Business Understanding Growing With It?Every enterprise wants trusted Business Intelligence, reliable Enterprise AI, and a single source of truth. Yet most organizations still struggle with conflicting dashboards, duplicated business logic, inconsistent KPIs, fragmented Semantic Modeling, weak Data Governance, and AI systems that cannot reliably understand enterprise data.If you're tired of building reports that never agree, rewriting the same business metrics across multiple tools, or wondering why your AI projects produce inconsistent results, this book was written for you.Rather than focusing on a single vendor or platform, Engineering Semantic Data Layers teaches you how to design a production-grade Semantic Data Layer that becomes the trusted foundation for Business Intelligence, Analytics Engineering, Enterprise Data Architecture, modern Data Products, AI agents, Retrieval-Augmented Generation (RAG), and intelligent enterprise applications.You'll learn how to engineer scalable semantic platforms that deliver governed business definitions, reusable metrics, trusted business logic, and consistent semantic models across modern cloud ecosystems. Whether you're building enterprise reporting solutions, designing AI Data Architecture, or integrating a Knowledge Graph into intelligent applications, the principles in this book remain practical, scalable, and platform-independent.Inside this book, you'll discover how to: Design enterprise-grade Semantic Data Layers from the ground upMaster Semantic Modeling for reusable business entities, dimensions, measures, and KPIsBuild governed Data Products that serve analytics, applications, and AI consistentlyDevelop modern Enterprise Data Architecture using platform-neutral engineering principlesImplement robust Data Governance, metadata management, lineage, and business glossariesApply Analytics Engineering best practices for scalable, maintainable semantic platformsIntegrate semantic architectures with Microsoft Fabric, Power BI, Snowflake, Databricks, dbt, Tableau, Looker, and CubeBuild AI Data Architecture that supports RAG, Text-to-SQL, Model Context Protocol (MCP), Knowledge Graph solutions, and AI agentsOptimize performance, observability, CI/CD, and production operations for enterprise deploymentsModernize legacy analytics environments while preparing your organization for the future of Enterprise AIWhether you're a Data Engineer, Analytics Engineer, BI Developer, Data Architect, Platform Engineer, AI Engineer, Solution Architect, or Technical Leader, this book provides the production-grade engineering knowledge needed to design semantic platforms that power modern data ecosystems and intelligent enterprises.Stop treating semantics as an afterthought. Build the Semantic Data Layer that powers trusted Business Intelligence, accelerates Enterprise AI, and becomes the foundation of your organization's modern data platform. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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

Condición: New. Print on Demand.

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

Condición: New. Print on Demand.

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

PAP. Condición: 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. Condición: new. Paperback. Your Data Is Growing. But Is Your Business Understanding Growing With It?Every enterprise wants trusted Business Intelligence, reliable Enterprise AI, and a single source of truth. Yet most organizations still struggle with conflicting dashboards, duplicated business logic, inconsistent KPIs, fragmented Semantic Modeling, weak Data Governance, and AI systems that cannot reliably understand enterprise data.If you're tired of building reports that never agree, rewriting the same business metrics across multiple tools, or wondering why your AI projects produce inconsistent results, this book was written for you.Rather than focusing on a single vendor or platform, Engineering Semantic Data Layers teaches you how to design a production-grade Semantic Data Layer that becomes the trusted foundation for Business Intelligence, Analytics Engineering, Enterprise Data Architecture, modern Data Products, AI agents, Retrieval-Augmented Generation (RAG), and intelligent enterprise applications.You'll learn how to engineer scalable semantic platforms that deliver governed business definitions, reusable metrics, trusted business logic, and consistent semantic models across modern cloud ecosystems. Whether you're building enterprise reporting solutions, designing AI Data Architecture, or integrating a Knowledge Graph into intelligent applications, the principles in this book remain practical, scalable, and platform-independent.Inside this book, you'll discover how to: Design enterprise-grade Semantic Data Layers from the ground upMaster Semantic Modeling for reusable business entities, dimensions, measures, and KPIsBuild governed Data Products that serve analytics, applications, and AI consistentlyDevelop modern Enterprise Data Architecture using platform-neutral engineering principlesImplement robust Data Governance, metadata management, lineage, and business glossariesApply Analytics Engineering best practices for scalable, maintainable semantic platformsIntegrate semantic architectures with Microsoft Fabric, Power BI, Snowflake, Databricks, dbt, Tableau, Looker, and CubeBuild AI Data Architecture that supports RAG, Text-to-SQL, Model Context Protocol (MCP), Knowledge Graph solutions, and AI agentsOptimize performance, observability, CI/CD, and production operations for enterprise deploymentsModernize legacy analytics environments while preparing your organization for the future of Enterprise AIWhether you're a Data Engineer, Analytics Engineer, BI Developer, Data Architect, Platform Engineer, AI Engineer, Solution Architect, or Technical Leader, this book provides the production-grade engineering knowledge needed to design semantic platforms that power modern data ecosystems and intelligent enterprises.Stop treating semantics as an afterthought. Build the Semantic Data Layer that powers trusted Business Intelligence, accelerates Enterprise AI, and becomes the foundation of your organization's modern data platform. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…