Overview

LLMOps for Engineers Build Reliable, Scalable, and Cost-Efficient LLM Systems That Deliver Real Business Value What happens when your LLM system starts drifting, costs spike overnight, or a small prompt change unexpectedly breaks production? Every engineering team building AI features eventually faces the same challenge: how do you operate large-language-model systems with the same rigor, reliability, and clarity expected of mature software platforms? This book gives you that framework. LLMOps for Engineers provides a complete, engineer-ready blueprint for building, deploying, and maintaining LLM systems that actually hold up under real-world constraints. Instead of vague theory or one-off examples, you'll find concrete practices for prompt versioning, RAG pipelines, embedding management, evaluation workflows, observability, governance, and cost control, practices you can implement immediately. Designed for data scientists, ML engineers, software developers, DevOps teams, and platform engineers, this book shows you how to turn LLM applications into stable, scalable, and auditable systems. You'll learn how to manage model drift, fine-tuning updates, multi-modal inputs, hybrid local-and-cloud routing, and incident response with clarity and confidence. If your goal is to deliver dependable AI features without runaway complexity or unpredictable cost, this guide gives you the tools and patterns to get there. You will learn how to: - Build reproducible pipelines for prompts, embeddings, and RAG retrieval that behave consistently across environments. - Implement versioning systems for prompts, models, and corpora that prevent silent regressions. - Evaluate and monitor LLM behaviour using meaningful metrics beyond accuracy, including groundedness, hallucination rate, and cost per request. - Deploy LLM systems using cloud, on-prem, hybrid, or edge architectures aligned with business and regulatory requirements. - Reduce costs through batching, routing, quantisation, and strategic use of smaller local models where appropriate. - Establish clear workflows across data science, ML engineering, DevOps, and platform engineering teams, ensuring smooth handoffs and accountable ownership. - Build incident-ready systems with guardrails, audit logs, canary releases, and rollback-friendly design patterns. If you want to create AI features that stand up to production load, scale with demand, and deliver measurable business outcomes, not unpredictable experiments, this book shows you how. Get your copy of LLMOps for Engineers today and start building LLM systems that work reliably, efficiently, and at scale.

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