# AI Model Evaluation (Manning)

**URL:** <https://forum.devtalk.com/t/ai-model-evaluation-manning/211571>\
**Category:** AI Learning Resources\
**Tags:** responsible-ai, published-book, manning, machine-learning, ai-evaluations, human-in-the-loop, ai-model-evaluation, mlops, ml-observability, llm-evaluation\
**Created:** [20 August 2025 13:32 UTC](https://forum.devtalk.com/t/ai-model-evaluation-manning/211571 "2025-08-20T13:32:18Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![ManningBooks](https://forum.devtalk.com/user_avatar/forum.devtalk.com/manningbooks/32/1650_2.png) [@ManningBooks](https://forum.devtalk.com/u/ManningBooks)\
**Post date:** [21 August 2025 07:59 UTC](https://forum.devtalk.com/t/ai-model-evaluation-manning/211571/3 "2025-08-21T07:59:32Z")

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Definitely. Here are some questions to help your team that the book addresses clearly:

1. What happens if your model is “accurate” offline but tanks your engagement metrics in production — how would you know why?  
(Follow-up: Do you have evaluation strategies beyond just accuracy or F1?)

2. When was the last time your team measured the system latency impact of a new AI model before launching it?  
(And what if the model slowed down page load time by 200ms — would you catch it before it hits users?)

3. If a model makes worse predictions for a specific user segment, do you catch that in your current evaluation process? Or are those failures only visible after a launch?

4. Before you ship a model, do you know how it affects:

- Feature latency?
- Cold start performance?
- Infrastructure cost at scale?  
(Or are you finding out during the fire drill after launch?)

Are you still using the same evaluation metrics your team used 3 years ago?  
(What if the nature of your product or user behavior has changed — and your evaluations are now stale?)

Hope this helps.

Cheers

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