This book presents a practitioner-oriented treatment of LLMOps: the engineering discipline required to deploy, scale, and govern large language model systems in production. Advanced Large Language Model Operations explains why LLM deployments differ from classical MLOps — due to cost/latency economics, non-deterministic behavior, retrieval and tool-calling pipelines, and new security and compliance threat models — and translates these realities into concrete operational patterns, metrics, and decision frameworks for real-world systems.

The text is organized into four parts spanning foundations, production delivery, optimization, and governance, culminating in a capstone implementation. It uses Ishtar AI, a high-stakes, evidence-grounded journalism assistant, as a running case study to connect theory to practice across infrastructure and environment design, CI/CD and continuous evaluation, observability, scaling, performance optimization, retrieval-augmented generation, multi-agent orchestration, robustness testing, and ethical/responsible deployment.

Advanced Large Language Model Operations offers an essential and in-depth roadmap for the deployment, management, and optimization of large language model (LLM) systems in enterprise and research settings. Bridging the persistent gap between model development and real-world application, this authoritative volume walks readers through the entire lifecycle of operationalizing LLMs, from foundational infrastructure and environment design to advanced strategies for monitoring, scaling, and optimization.  

Each chapter includes actionable checklists, advanced optimization techniques, and case-based insights that demonstrate both the successes and pitfalls of real-world LLM deployments. Readers with a strong grounding in machine learning and programming will gain the expertise to integrate LLMOps into their workflows, reduce deployment times, maximize scalability, and sustain high-performing language model solutions in production environments. 

David Stroud is a Staff Software Engineer at Fox Corporation in New York City, where he designs and deploys enterprise-grade generative AI systems with a focus on production LLM reliability, evaluation-driven release processes, and scalable, cost-aware infrastructure. He is the author of Advanced Large Language Model Operations: Best Practices and Innovative Strategies (Springer), drawing directly on real-world deployments to translate LLM research advances into repeatable operational playbooks.

His applied research spans NLP, deep learning, and reinforcement learning, including toxic comment classification, AWS EC2 spot price forecasting with LSTMs, Deep RL for intelligent portfolio management, IMU-based stationary exercise classification, and adversarially trained CNN approaches for PDF/OCR reading. Additional work addresses freight optimization and macroeconomic prediction (e.g., RL approaches to the U.S. GDP output gap). Across industry and research, his work emphasizes rigorous evaluation, measurable performance, and production readiness for real systems.