Why is training accuracy not enough as a production health signal?
PICTURE THIS: DATA SPLIT
Fit on train, tune on val, report on test once.
Simple meaning
Training accuracy is a historical number on a static split.
WHY — Monitoring instead of guessing?
Why interviewers care about Monitoring:
on Monitoring.
the situation, the default choice, and one exception - that reads as experience.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1Training accuracy is a
historical number on a static split.
- 2Live traffic, delayed labels,
and pipeline bugs can all degrade quality while that number never changes.
- 3Production needs live telemetry
on data and predictions.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
Training accuracy is a historical number on a static split. Live traffic, delayed labels, and pipeline bugs can all degrade quality while that number never changes.