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.
| ISBN: | 9783032238290 |
| Publication date: | 17th February 2027 |
| Author: | David Stroud |
| Publisher: | Springer an imprint of Springer Nature Switzerland |
| Format: | Hardback |
| Pagination: | 667 pages |
| Series: | The Springer Series in Applied Machine Learning |
| Genres: |
Artificial intelligence Natural language and machine translation Machine learning Information technology industries Business innovation |
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.
Advanced Large Language Model Operations features in the following genres: Artificial intelligence, Natural language and machine translation, Machine learning, Information technology industries, Business innovation
Hardback. £49.49, down from the £54.99 cover price. Not Available.
Advanced Large Language Model Operations was written by David Stroud and published by Springer an imprint of Springer Nature Switzerland
Advanced Large Language Model Operations has 667 pages
Yes it is part of The Springer Series in Applied Machine Learning series
£49.49, reduced from £54.99. Not Available.