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