Machine Learning
scikit-learn pipelines combine preprocessing steps and models into a single object. They're elegant for training and inference, but they introduce testing challenges: how do you test a transformer in isolation? How do you verify that the pipeline doesn't leak information? How do you catch regressions when
Machine Learning
Feature stores solve the problem of feature reuse and training/serving consistency in ML systems. They also introduce a new category of bugs: features that compute correctly in isolation but produce wrong values when served at inference time due to point-in-time leakage, online/offline discrepancies, or stale data. Testing a
Machine Learning
A model you deploy today will degrade over time. The world changes — user behavior shifts, economic conditions fluctuate, upstream data systems evolve. The model doesn't know. Without drift monitoring, you find out when a user complains or a business metric tanks. Drift detection is the practice of monitoring
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. Model validation is the discipline of measuring that generalization — reliably, without cheating. This guide covers the core validation techniques, when to use each, and the
Machine Learning
ML models fail silently when their input data changes. A column that was never null suddenly has 30% nulls. A categorical feature gains new values the model was never trained on. A numeric distribution shifts because a upstream system changed its calculation. Data quality testing catches these problems before they
Machine Learning
Offline evaluation tells you a model performs better on held-out data. Production A/B testing tells you it actually improves the business metric you care about. These are different questions, and sometimes they have different answers. A new recommendation model might have higher offline AUC but lower click-through rate in