Overview

Modern LLMOps Architecture: Design and Operate Robust LLM Platforms That Reduce Risk, Control Costs, and Improve Output Quality offers a practical, hands-on guide for engineers, data scientists, and AI architects who want to build production-grade large language model systems with confidence. Are you struggling to scale LLM systems while keeping costs under control, output reliable, and operations secure? Modern LLM platforms are powerful, but without the right architecture, governance, and operational strategies, they can become expensive, unpredictable, and difficult to manage. This book provides the frameworks, workflows, and real-world practices necessary to operate LLM platforms efficiently and safely. You'll explore a complete approach to LLMOps, from ingesting and versioning datasets to orchestrating multiple models, managing prompts, implementing retrieval-augmented generation, and monitoring system performance. Learn how to design architectures that balance latency, cost, and quality, implement robust fallback and recovery strategies, and enforce ethical and security standards across your platform. By reading this book, you will gain the ability to: Design scalable, resilient LLM platforms tailored to your enterprise needs. Implement end-to-end data, prompt, and model management pipelines. Optimize model performance and cost through multi-model orchestration and provider switching. Build comprehensive monitoring, observability, and human-in-the-loop feedback systems. Apply governance frameworks to enforce ethical, secure, and compliant operations. Future-proof your LLM infrastructure with strategies for migration, continual improvement, and team upskilling. Packed with actionable guidance, detailed architectural diagrams, and implementable code examples, this book serves as both a reference and a blueprint for modern LLMOps. Whether you're launching your first production system or refining an existing platform, the principles in this guide help reduce operational risk, control costs, and improve output quality. Take control of your enterprise LLM operations today. Apply proven frameworks, adopt best practices, and ensure your LLM systems deliver reliable, cost-efficient, and high-quality results at scale.

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