Overview

Take generative AI from prototype to production with confidence, master core LLM architectures, rigorous evaluation (offline and A/B testing), LLMOps and deployment pipelines, and the reliability practices that keep systems stable, secure, and scalable in the real world. Key Features Turn generative AI prototypes into production-ready applications Master LLM evaluation, observability, and reliability engineering Deploy and scale AI systems using LLMOps and modern DevOps tools Book DescriptionVibe-coding tools & coding assistants make it easy to spin up generative AI prototypes. Getting those prototypes into production is where most teams stall. This book is a practical guide to building production-ready generative AI applications that are reliable, scalable, and secure, and to understanding where traditional software best practices can clash with the realities of operating LLM-based systems. Written by a Staff AI Engineer at Google, it takes you through the full AI product lifecycle: scoping and building effective prototypes, aligning them with business goals, and scaling enterprise-wide generative AI adoption. You will learn how to evaluate LLMs with offline metrics, human-in-the-loop methods, and statistical testing. Next, you will design core architectures such as RAG, vector databases, agents, and memory systems. Next, operationalize these systems with production-grade code, robust testing, DevOps, MLOps, and LLMOps workflows, including deployment and scaling on modern LLMOps platforms. The book also covers security, Responsible AI, and modern observability and reliability for generative AI systems. By the end you’ll learn how to run post-launch A/B tests, maintain systems over time, and measure business impact. The focus is on durable engineering principles, so your products succeed beyond the prototype stage.What you will learn Design offline and online evaluation strategies (including statistical A/B testing) and collect the right data Convert AI prototypes into production-ready applications that are stable, scalable, & secure Reduce maintenance effort with best practices in testing, configuration, and code readability Implement DevOps, MLOps, and LLMOps—what's common and what differs across these approaches for AI systems Build platform teams to scale enterprise-wide generative AI adoption Define reliability targets using SRE principles and statistical A/B testing Who this book is forThis book is for technical leaders, AI engineers, data scientists, software engineers, and architects building generative AI applications. It is also ideal for engineering managers, product leaders, and technical decision-makers who need to understand how to deploy, scale, and maintain production-grade AI systems.

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9781806678655

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Leonid Kuligin is a staff AI engineer at Google Cloud, working on generative AI and classical machine learning solutions (such as demand forecasting or optimization problems). Leonid is one of the key maintainers of Google Cloud integrations on LangChain, and a visiting lecturer at CDTM (TUM and LMU). Prior to Google, Leonid gained more than 20 years of experience in building B2C and B2B applications based on complex machine learning and data processing solutions such as search, maps, and investment management in German, Russian, and US technological, financial, and retail companies.

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