Lilly Tech Systems
Feature Stores
Master feature management for machine learning — from Feast and Tecton to online/offline serving, feature engineering pipelines, and production best practices.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is a feature store? Why feature management matters, training-serving skew, and the feature store landscape.
2. Feast
The open-source feature store: setup, feature definitions, materialization, and retrieval for training and serving.
3. Tecton
Enterprise feature platform: real-time features, stream processing, feature monitoring, and team collaboration.
4. Feature Engineering
Designing features for feature stores: transformations, aggregations, entity design, and feature pipelines.
5. Online/Offline Serving
Online stores for real-time serving, offline stores for training, materialization strategies, and consistency.
6. Best Practices
Feature naming, versioning, discovery, monitoring, governance, and production operations.
What You'll Learn
By the end of this course, you'll be able to:
Feature Management
Define, store, version, and serve ML features using a centralized feature store.
Feast & Tecton
Set up and use open-source (Feast) and enterprise (Tecton) feature store platforms.
Training-Serving Consistency
Eliminate training-serving skew by using the same features for both training and inference.
Production Operations
Monitor feature freshness, quality, and drift in production ML systems.