Managing Production Large Language Models: Designing, Deploying, and Operating LLM Systems at Scale

Managing Production Large Language Models is the operational engineering manual for the teams responsible for designing, deploying, and running large language models inside enterprise production systems. Written for ML architects, ML and LLM engineers, technical team leads, and platform and SRE engineers, the book treats LLM operations as a structural discipline that spans selection, adaptation, serving, evaluation, observability, safety, governance, and cost—not a vendor or framework conversation.

The book opens with the operational gap that defines the field. LLM features fail in distinct ways: retrieval pipelines return stale documents, autonomous agents enter retry loops on rate-limited APIs, prompt-injection vulnerabilities exfiltrate sensitive context, hallucinated outputs reach regulated users, and token budgets blow through quarterly forecasts in a single weekend. From that opening, twelve chapters in four parts develop the patterns that close those gaps.

Part I, Foundations (Chapters 1–4), establishes the vocabulary the rest of the book depends on: the production LLM landscape, deployment model decisions (proprietary API, hosted open weights, self-hosted), the prompting and context-engineering primitives, and the adaptation spectrum from prompt engineering to RAG to parameter-efficient fine-tuning to full fine-tuning. Part II, Adaptation and Serving (Chapters 5 and 6), covers the infrastructure that determines whether an LLM feature survives real traffic—serving architectures, latency budgets, batching and continuous batching, KV-cache management, token streaming, and the GPU economics of inference.

Part III, Production Concerns (Chapters 7–10), covers the disciplines the rest of the engineering organization will hold the LLM team accountable for: safety, security, and content governance (prompt-injection defense, content filtering, output redaction, audit logging); observability (tracing, evaluation telemetry, drift detection across embedding spaces); testing and evaluation (offline benchmarks, online experimentation, regression suites, human evaluation); and FinOps for LLM workloads (token-budget alerts, cache hit-rate SLOs, model-tier routing). Part IV, Frontier Patterns and Case Studies (Chapters 11 and 12), extends the patterns to agentic systems, multi-modal workloads, and emerging architectures, and synthesizes the book's patterns into reference architectures for common production shapes.

Every chapter opens with a composite production incident drawn from the shapes of real failures, develops the engineering response in vendor-neutral patterns, and closes with three structured artifacts: a Technical Checklist of conditions the team should verify before declaring a system production-ready, an Owner Matrix that names the role accountable for each artifact, and a Team Discussion Guide that surfaces misalignment before it surfaces as an incident. Where specific tools appear, they make abstract patterns concrete; the patterns themselves outlive the tools.

Readers leave with the deployment vocabulary to make selection and architecture decisions that age well, the serving and observability patterns to keep production LLMs reliable at scale, the safety and governance controls to defend them under regulatory and reputational scrutiny, and the FinOps discipline to keep them economically viable as the portfolio expands.

© 2026 PublishDrive (E-bog): 9798904981068

Udgivelsesdato

E-bog: 26. maj 2026