04What does a CI/CD pipeline for ML add over a standard software CI/CD pipeline?▼medium★ EssentialMicrosoftDatabricksGoogle2 repliesunlockedThe trap is describing ordinary software CI/CD. The signal is the three additions ML brings: data validation, a model-quality gate against a baseline, and versioning data plus model plus code as a unit. Here is what truly differs.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 but a quiet upstream data change. The signal is validating at ingestion (schema plus distribution), data contracts with producers, and failing loud rather than 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. What counts is naming the test layers that catch what the headline metric can't. Here is the framework that gets scored highest.Open full answer →
