Feature Engineering

Feature engineering is the process of transforming raw data into the input representations that machine learning models use to make predictions. It includes selecting relevant variables, creating derived features, handling missing values, encoding categoricals, and scaling numerical inputs.

Feature engineering was historically the most time-intensive part of ML development — responsible for 80% of a data scientist's time in the pre-LLM era. Deep learning and large language models have reduced but not eliminated it: structured-data ML (fraud detection, demand forecasting, risk scoring) still depends heavily on hand-crafted features. Feature stores (Feast, Tecton, Databricks Feature Store) industrialize feature reuse across models and teams.