What tests belong in an ML pull request pipeline?
PICTURE THIS: GIT FLOW
Simple meaning
Unit tests for transforms, schema tests on sample data, a tiny training job that must beat a dummy baseline, and a serving smoke test that loads the artifact.
WHY — CI/CD for ML instead of guessing?
Why interviewers care about CI/CD for ML:
separate people who only read docs from people who shipped.
and tied to MLOps work.
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:
- 1Unit tests for transforms,
schema tests on sample data, a tiny training job that must beat a dummy baseline, and a serving smoke test that loads the artifact.
- 2You can also fail
the build if a forbidden dependency like unpinned CUDA appears.
- 3Full-scale training stays on
a nightly or merge-to-main workflow.
- 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
Unit tests for transforms, schema tests on sample data, a tiny training job that must beat a dummy baseline, and a serving smoke test that loads the artifact. You can also fail the build if a forbidden dependency like unpinned CUDA appears.