ML and Generative AI in the Data Lakehouse

Chapter 5. Feature Engineering in the Unity Catalog

Feature Engineering in Unity Catalog serves as a critical component for feature engineering and management in the Lakehouse architecture. Feature engineering remains a cornerstone of ML success, since models depend on well-preprocessed features to deliver accurate predictions. Feature Engineering in Unity Catalog provides a centralized repository for managing features, ensuring consistency between training and serving workflows, and enabling collaboration across teams. Because feature tables are standard Delta tables in Unity Catalog, they inherit the full governance stack automatically: access control, metadata tracking, lineage, and reusability across workspaces.

In this chapter, you will explore how Feature Engineering in Unity Catalog enhances multiple aspects of the ML lifecycle. This includes creating and managing feature tables, using feature lookups to generate training datasets, and enabling real-time inference with online feature stores. The integration with MLflow ensures that feature metadata is retained throughout the deployment process, streamlining scoring and enabling reproducible workflows. You’ll conclude this chapter with a practical example where you use the LendingClub dataset to create a feature table and use the features in that table to build a model that predicts loan defaults.

The Role of Feature Engineering in ML

ML models do not operate on raw data. Instead, they depend on features, which are specific ...