ML Testing
You've trained a new model. Offline metrics look better than the current production model. But will it actually perform better with real users and real traffic? Shadow deployment answers that question without risk. You run the new model in parallel with production, compare their predictions, and collect real-
ML Testing
ML models trained on historical data inherit historical biases. A hiring model trained on past decisions might discriminate by gender. A credit scoring model might perpetuate racial bias from historical lending practices. A medical risk model might underperform for underrepresented demographic groups. Testing for bias isn't optional — it&
AI Testing
Computer vision models break in ways that unit tests don't catch. Your preprocessing pipeline silently changes a pixel value range, your bounding box coordinates are in the wrong format, or your model returns detections in a different order than your downstream code expects. None of these show up
Machine Learning
A machine learning model that performs well on training data is meaningless. What matters is whether it generalizes to data it hasn't seen.