Pipeline Design & Transformation

A machine learning pipeline is a sequence of stateful transformations. Understanding how to compose fit/transform steps, avoid leakage across pipeline boundaries, and apply transformations consistently between training and inference separates toy experiments from production systems.

Learning Objectives

• →Compose a multi-step fit/transform pipeline without data leakage
• →Apply min-max scaling with training statistics
• →Implement a polynomial feature expansion for interaction terms
• →Reproduce a pipeline at inference time given only the fitted artifacts

Practice