Problem
Scenario
Your team has trained a model and offline results look promising. Before shipping it, you need to decide whether the model is actually good enough for production and what evidence would justify deployment.
Question
How would you evaluate whether a model is suitable for deployment?
What You Should Consider
- Discrimination metrics such as AUC-ROC
- Operating-point metrics such as precision and recall
- Calibration quality
- Threshold choice relative to business costs
- Comparison to the current production baseline
Practicing as: AI Engineer interview at FortinetHi, I'll play your Fortinet interviewer for the AI Engineer role. Candidates describe these interviews as mostly positive and moderately difficult, so expect me to be friendly and conversational. Take your time with the question above and answer like we're in the room.
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