Reusable Feature Pipelines

Day 46 of 100 Days of MLOps. On Day 15 you bundled preprocessing and model together. Today you separate them: a feature pipeline you fit once and save as its own artifact, so the exact same transformations apply in training and serving — and can be shared across models. It remembers its training statistics, which is what keeps features consistent everywhere. The on-ramp to feature stores. Runs 100% locally on any OS.