Testing
Most ML projects have zero tests for their scikit-learn pipelines. The model "works" if it trains without crashing and the accuracy metric looks reasonable. But this leaves a class of bugs invisible: * A custom transformer that silently drops rows * A Pipeline that applies preprocessing in the wrong
Machine Learning
scikit-learn pipelines combine preprocessing steps and models into a single object.
Machine Learning
Feature stores solve the problem of feature reuse and training/serving consistency in ML systems.
Machine Learning
A model you deploy today will degrade over time. The world changes — user behavior shifts, economic conditions fluctuate, upstream data systems evolve.
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.
Machine Learning
ML models fail silently when their input data changes. A column that was never null suddenly has 30% nulls.
Machine Learning
Offline evaluation tells you a model performs better on held-out data.