Module 03
Data Modeling
Design data models that support analytics, feature engineering, training, and serving without leakage or excessive complexity.
7 lessons · 7 videos · 54 min- 03.01
Entities, Events, and Observations
Define entities, events, observations, labels, and timestamps for a supervised ML problem.
- 03.02
Star Schemas for Analytics and ML
Design a star schema with fact tables and dimension tables that can support both BI queries and feature generation.
- 03.03
Normalization vs Denormalization for ML
Choose between normalized and denormalized representations based on update frequency, query patterns, training cost, and leakage risk.
- 03.04
Slowly Changing Dimensions
Model changing attributes using Type 1 and Type 2 slowly changing dimensions for point-in-time feature correctness.
- 03.05
Feature Store Data Models
Define feature views, entities, feature values, timestamps, and online keys for a feature store.
- 03.06
Label Modeling and Leakage
Create a label table with observation windows and prediction windows that avoids target leakage.
- 03.07
Modeling for Multi-Tenant Products
Add tenant boundaries, access controls, and partitioning strategies to a data model used by multiple customers.