Advanced Large Language Model Operations
Best Practices and Key Concepts
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Beschreibung
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.
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.
Translates LLM theory into proven operational frameworks for real-world deployment and governance Bridges the gap between model development and production through end-to-end LLMOps best practices Features actionable checklists and a running case study to master scalable, compliant, and robust LLM systems
Autor*in
David Stroud
Themen in »Advanced Large Language Model Operations«
Large Language Model Operations (LLMOps) Production LLM systems Retrieval-Augmented Generation (RAG) Enterprise search Vector databases Embeddings Prompt engineering Prompt management and versioning Tool calling / function calling Agentic workflows Multi-agent orchestration LangChain / LangGraph Evaluation and benchmarking LLM-as-a-Judge Groundedness and citation quality