Module 11
Feature Engineering & Stores
Design, compute, store, serve, and monitor features for offline training and online inference.
7 lessons · 4 videos · 51 min- 11.01
Feature Engineering Lifecycle
Describe the lifecycle of a feature from definition and computation to validation, serving, monitoring, and deprecation.
- 11.02
Offline vs Online Features
Determine whether a feature should be computed offline, online, on demand, or through a hybrid batch-stream pipeline.
- 11.03
Feast Feature Store Walkthrough
Define Feast entities, feature views, data sources, and retrieval logic for both training and online inference.
- 11.04
Tecton and Managed Feature Platforms
Compare managed feature platform capabilities including transformation management, online serving, monitoring, and governance.
- 11.05
Point-in-Time Feature Retrieval
Generate a point-in-time correct training dataset from historical feature values and labeled events.
- 11.06
Online Serving and Low-Latency Access
Design an online feature serving path using entity keys, freshness constraints, cache behavior, and latency budgets.
- 11.07
Feature Reuse, Discovery, and Governance
Create feature metadata that enables discovery, ownership, reuse, quality tracking, and safe deprecation.