How do you gate a model so a worse candidate cannot reach production?
PICTURE THIS: DATA SPLIT
Fit on train, tune on val, report on test once.
Simple meaning
After training, an evaluation step compares the candidate to the current champion on a frozen eval set and required slices.
WHY — CI/CD for ML instead of guessing?
Why interviewers care about CI/CD for ML:
question about CI/CD for ML.
trade-offs, and what you would actually do on a MLOps project - not buzzwords.
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:
- 1After training, an evaluation
step compares the candidate to the current champion on a frozen eval set and required slices.
- 2Only if metrics pass
thresholds plus a human or automated approval does the registry stage move to Production.
- 3CD then deploys that
version, never the latest folder on disk.
- 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
After training, an evaluation step compares the candidate to the current champion on a frozen eval set and required slices. Only if metrics pass thresholds plus a human or automated approval does the registry stage move to Production.