{"product_id":"the-llm-engineers-handbook-self-hosted-ai-in-production-professional-techniques-for-deploying-customizing-and-fine-tuning-llama-mistral-and-ope-9798277720141","title":"The LLM Engineer's Handbook: Self-Hosted AI in Production: Professional Techniques for Deploying, Customizing, and Fine-Tuning LLaMA, Mistral, and Ope","description":"\u003cp\u003e • Author(s): Amaris Quill\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Data Science - Neural Networks\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMaster the complete lifecycle of self-hosted large language model deployments-from infrastructure design to production operations.\u003c\/b\u003e\u003cbr\u003eIn an era where data sovereignty, security compliance, and cost control are paramount, organizations are increasingly moving away from cloud-based API services toward self-hosted AI infrastructure. \u003ci\u003eThe LLM Engineer's Handbook\u003c\/i\u003e is the definitive technical guide for engineers, architects, and technical leaders who need to deploy, optimize, and maintain production-grade LLM systems within their own infrastructure.\u003cbr\u003eThis comprehensive resource bridges the gap between theoretical AI concepts and real-world implementation, providing battle-tested strategies for running models like LLaMA, Mistral, and other open-source language models in secure, on-premises environments. Whether you're building HIPAA-compliant healthcare systems, implementing air-gapped deployments for government applications, or optimizing inference costs for high-throughput enterprise services, this book delivers the practical knowledge you need. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhat You'll Learn: \u003c\/b\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eInfrastructure Design\u003c\/b\u003e: Plan and build GPU clusters with optimal hardware configurations, network topologies, and cooling systems for cost-effective, high-performance deployments\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eSecurity \u0026amp; Compliance\u003c\/b\u003e: Implement enterprise-grade security frameworks including air-gapped architectures, encryption standards, and compliance tracking for GDPR, HIPAA, and SOC 2\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eModel Optimization\u003c\/b\u003e: Master quantization techniques (GPTQ, GGUF, AWQ) to reduce memory footprint while preserving model quality, and implement advanced inference optimizations like Flash Attention and speculative decoding\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eProduction Serving\u003c\/b\u003e: Design robust API gateways, implement load balancing strategies, and deploy inference servers (vLLM, TGI, Triton) that scale from prototype to production\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eFine-Tuning at Scale\u003c\/b\u003e: Apply LoRA, QLoRA, and RLHF techniques to customize models for domain-specific applications while managing distributed training infrastructure\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eAdvanced Architectures\u003c\/b\u003e: Build RAG systems with vector databases, implement multi-model routing strategies, and orchestrate complex agent-based workflows\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eOperations Excellence\u003c\/b\u003e: Establish comprehensive monitoring, observability, and incident response procedures to maintain reliable production systems\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eWho This Book Is For: \u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eMachine learning engineers transitioning from cloud APIs to self-hosted infrastructure\u003c\/li\u003e\n\u003cli\u003eDevOps and platform engineers building AI infrastructure for their organizations\u003c\/li\u003e\n\u003cli\u003eTechnical architects designing secure, compliant AI systems for regulated industries\u003c\/li\u003e\n\u003cli\u003eData scientists seeking to understand production deployment considerations\u003c\/li\u003e\n\u003cli\u003eEngineering leaders evaluating build-vs-buy decisions for LLM capabilities\u003c\/li\u003e\n\u003c\/ul\u003eUnlike generic AI tutorials focused on high-level concepts or cloud-hosted solutions, this handbook provides the deep technical detail required for successful self-hosted deployments. Every chapter includes practical implementation guidance, architectural decision frameworks, and real-world trade-off analysis to help you navigate the complexities of production LLM systems. \u003cp\u003e\u003c\/p\u003eFrom selecting the right GPU hardware and configuring quantization parameters to implementing fault-tolerant training pipelines and debugging inference bottlenecks, \u003ci\u003eThe LLM Engineer's Handbook\u003c\/i\u003e equips you with the expertise to build AI systems that meet enterprise requirements for performance, security, and reliability-all while maintaining complete control over your data and infrastructure.","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":46861739032727,"sku":"9798277720141","price":2081.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798277720141.webp?v=1769963289","url":"https:\/\/atlanticbooks.com\/products\/the-llm-engineers-handbook-self-hosted-ai-in-production-professional-techniques-for-deploying-customizing-and-fine-tuning-llama-mistral-and-ope-9798277720141","provider":"Atlantic Books","version":"1.0","type":"link"}