Overview

In today's rapidly evolving AI landscape, building models that perform well in research labs is no longer enough. Real-world applications demand systems that are reliable, safe, and scalable under pressure. Trustworthy AI in Practice: Blueprints for Metrics, Pipelines, and Governance in Large-Scale Systems provides the blueprint for moving beyond guesswork and ad-hoc evaluation, giving AI engineers, researchers, and architects the tools to deploy systems they can truly trust. This hands-on guide walks you through designing and implementing full-stack evaluation pipelines for large language models, retrieval-augmented generation systems, and autonomous agents. You'll learn to measure performance not just by accuracy, but by grounded-ness, safety, reasoning quality, and operational stability-all with reproducibility, statistical rigor, and real-time monitoring in mind. Each concept is illustrated with actionable examples, templates, and ready-to-run workflows. Discover how to build automated judge frameworks using LLM-as-a-Judge, human-AI hybrid scoring, and robust rubric design. Integrate metrics, logging, and telemetry into CI/CD workflows to gain real-time observability and detect model drift before it impacts production. From dashboard visualization to incident response playbooks, this book teaches you to manage AI systems with the same operational discipline used in high-stakes software engineering. Beyond technical pipelines, this book also emphasizes governance and standards, showing you how to create approval workflows, maintain evaluation consistency, and scale oversight across teams and products. Learn how to align engineering, research, and product teams around transparent evaluation practices that reduce risk, improve safety, and foster trust with users. Packed with real-world case studies, practical templates, and expert insights, Trustworthy AI in Practice is the ultimate guide for anyone serious about deploying AI responsibly and effectively. Whether you're managing production-scale systems, developing next-generation models, or leading AI teams, this book gives you the frameworks, tools, and confidence to build AI systems that work-and keep working-when it matters most.

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