What automated tests belong in CI before deploying a classification model?
WHAT IT TESTS: Distinguishing code tests from ML-specific CI validation. ANSWER OUTLINE: Name data schema checks, performance regression vs baseline, bias audits, and artifact integrity. RED FLAG: Only testing the inference API while ignoring model behavior.
WHAT IT TESTS: Whether you know ML assets need validation beyond standard software tests in CI, covering data, model behavior, and artifacts. ANSWER OUTLINE: A strong answer names four checks: input schema and distribution validation; performance regression vs a baseline or champion; fairness, bias, or slice analysis; and artifact integrity checks like hash verification. RED FLAG: Confusing model testing with API tests and treating the model as an opaque binary without behavior validation.
Read the original → ml-ops.org
- #mlops
- #ci-cd
- #model-testing
- #machine-learning
- #deployment
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