SKU/Artículo: AMZ-B0GKWLMBY6
MODEL CONTEXT PROTOCOL: A PRACTICAL GUIDE TO BUILDING REAL AI SYSTEMS
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Kindle
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Fuera de stock
Fuera de stock
Peso con empaque:
0.76 kg
0.76 kg
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Sí
Sí
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- AI Systems That Don't Collapse in Production: Building Reliable AI Architecture with Model Context Protocol Get production-tested patterns for building AI systems that scale, remain debuggable, and control costs, using MCP and framework-agnostic principles. Key FeaturesReal production failure cases and recovery strategiesFramework-agnostic patterns for LangChain, MCP, custom AI stacksCost optimization and multi-model routing techniquesSecurity patterns for healthcare, finance, and legal complianceSystem design interview preparation for AI architecture rolesBook Description Building AI systems that work in production requires more than good prompts and API calls. It requires production-grade architecture. This book teaches you how to build reliable, scalable AI systems using Model Context Protocol (MCP) and framework-agnostic patterns. You'll learn the architectural principles that prevent silent failures, control costs, and handle real-world scale—before you ship to production. What's inside:Error handling that surfaces failures immediately, not after customer complaints.Multi-model routing to optimize cost versus quality.Context management for large documents within fixed windows.Building observability into AI workflows from day one.Scaling from prototype to production without rewrites.Security controls for regulated environments.Uses Model Context Protocol (MCP) to demonstrate production patterns that transfer across any AI stack, including LangChain, custom platforms, and internal tools. Production-ready skills you'll gain:Design AI systems that fail loudly instead of silently.Route requests across models based on cost and latency.Process arbitrarily large documents within context limits.Build debugging and tracing into AI pipelines that scale without throwing away your architecture.Implement enterprise security and governance.Pass senior AI system design interviews.Who this book is for:Backend developers integrating LLMs into production systems.Software architects designing scalable AI platforms.Engineering leads managing AI cost and reliability.Senior engineers preparing for AI architecture interviews.Requires basic backend development and REST API knowledge, and no prior AI experience is needed. Table of Contents Part I: Foundations - Why AI systems fail in production, MCP fundamentals Part II: Architecture - Client patterns, server design, tool architecture, context flow Part III: Building and Integrating - First MCP server, reliable tools, multi-agent systems, multi-model coordination, enterprise integration, memory architecture, production operations Part IV: Mastery and Reference - Security and governance, design patterns, system design interviews, next steps Appendices - Quick reference, code samples, tool templates, prompts and workflows, visual diagrams, troubleshooting guide, resources
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