Building a Machine Learning Pipeline with Python

Building machine learning pipelines is one of the most important skills for any data scientist.

In this post I walk through how to build a production-ready pipeline using Python and scikit-learn, covering data preprocessing, feature engineering, model selection, and deployment patterns that scale.

The key insight is to encapsulate all preprocessing steps inside the pipeline so training and inference are always in sync. This eliminates training-serving skew, one of the most common ML bugs in production.