Unit tests don’t catch ML failures.
Integration tests are slow.
Edge cases slip through.
I need better confidence.
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Unit tests don’t catch ML failures.
Integration tests are slow.
Edge cases slip through.
I need better confidence.
ML testing requires layered validation.
Test preprocessing, inference, and post-processing separately. Add data validation tests and sanity checks on outputs.
Use shadow deployments or replay historical traffic for realistic testing.
Common mistakes include: Treating ML like pure software, Testing only code paths, Skipping data validation
The takeaway is that ML systems fail differently and must be tested differently.