De-risk AI models, validate real-world performance, and align output with product goals.
Before you trust critical business systems to an AI model, you need to answer a few questions. Will it be fast enough? Will the system satisfy user expectations? Is it safe? Can you trust the output? This book will help you answer these questions and more before you roll out an AI system—and make sure it runs smoothly after you deploy.
In
AI Model Evaluation you’ll learn how to:
- Build diagnostic offline evaluations that uncover model behavior
- Use shadow traffic to simulate production conditions
- Design A/B tests that validate model impact on key product metrics
- Spot nuanced failures with human-in-the-loop feedback
- Use LLMs as automated judges to scale your evaluation pipeline
In
AI Model Evaluation author
Leemay Nassery shares her hard-won experiences specializing in experimentation and personalization across companies such as Spotify, Comcast, Dropbox, and Etsy. The book is packed with insights on what it really takes to get a model ready for production. You’ll go beyond basic performance evaluations to discover how you can measure model effectiveness on the product , spot latency issues as you introduce the model in your end-to-end architecture, and understand the model’s real‑world impact.