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End-to-End AI Evals: Build Metrics, Pipelines & Monitoring for Real-World LLM Systems
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Publisher: Independently Published
Published: Oct 23 2025
Pages: 250
Weight: 0.97
Height: 0.53 Width: 7.00 Depth: 10.00
Language: English
End-to-End AI Evals: Build Metrics, Pipelines & Monitoring for Real-World LLM Systems
How do you prove that your AI system works, not just in the lab, but in production, at scale, and under pressure? As models grow more capable and complex, evaluation has become the single most important ingredient separating experimental prototypes from dependable, deployable systems. Yet most teams still rely on ad-hoc tests, subjective judgment, and incomplete metrics.
This book gives you the blueprint to change that. End-to-End AI Evals shows you exactly how to design, automate, and govern evaluation workflows for large language models (LLMs) and agentic systems. You'll learn to move beyond static benchmarks toward continuous, data-driven evaluation pipelines that measure accuracy, grounded-ness, safety, and real-world performance, all with reproducibility, statistical rigor, and CI/CD integration in mind.
Built from the latest research and industry best practices, this hands-on guide walks you through every layer of the modern eval stack, from metric design and judge calibration to monitoring, drift detection, and rollback automation. Whether you're an AI engineer, researcher, or platform architect, you'll gain a practical framework for building systems you can actually trust.
You will learn how to:
Design, implement, and version full-stack evaluation pipelines for LLMs, RAG systems, and autonomous agents.
Create 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 for real-time observability.
Evaluate faithfulness, safety, and reasoning quality with reproducible metrics and test suites.
Build scalable dashboards, incident response playbooks, and governance structures for continuous evaluation.
Through real-world examples, code-complete templates, and ready-to-run workflows, this book teaches not only how to measure performance, but how to ensure confidence in every model you deploy.
If you're serious about production-grade AI systems, this is the missing manual for building evaluation pipelines that match the scale and ambition of your models.
Get your copy today, and start turning AI evaluation into a core engineering discipline, not an afterthought.
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