Build a scalable feature engineering pipeline with polars | Fullstory
Build a scalable feature engineering pipeline with polars | Fullstory
Feature engineering is the bridge between raw data and machine learning insights. This post demonstrates how to build production-ready feature engineering pipelines using Polars, a high-performance DataFrame library that balances speed with developer experience.
While Python offers several frameworks for data processing (pandas for ease of use, PySpark for distributed computing), Polars provides an efficient middle ground for building scalable, maintainable pipelines. The article covers practical techniques for parsing JSON data with JSONPath syntax and transforming event streams into powerful ML-ready features. Learn how to convert every click, scroll, and pause into predictive insights using a modern approach to feature engineering.
