04What does a CI/CD pipeline for ML add over a standard software CI/CD pipeline?▼medium★ EssentialMicrosoftDatabricksGoogle2 repliesunlockedThe trap is describing plain software CI/CD. The signal is the three things ML adds: data validation, a model-quality gate against a baseline, and versioning data plus model plus code together. Here is what is genuinely different.Open full answer →
10How do you catch a broken upstream data change before it silently degrades your model?▼medium★ EssentialDatabricksMicrosoftAmazon2 repliesunlockedThe most common ML production failure is not a code bug, it is a quiet upstream data change. The signal is validating at ingestion (schema plus distribution), data contracts with producers, and failing loud instead of training on garbage.Open full answer →
16How do you test an ML system (beyond accuracy), including data, model, and behavioral tests?▼medium★ EssentialGoogleMicrosoftDatabricks3 replies○ sign inA high accuracy number hides slice failures, brittleness, and silent data bugs. The signal is naming the test layers that catch what the headline metric can't. Here is the framework that gets scored highest.Open full answer →
