Lilly Tech Systems
Designing ML Feature Stores
Master the architecture and implementation of production feature stores for machine learning. Learn to solve training-serving skew, build offline and online feature infrastructure, implement real-time feature pipelines, and deploy feature platforms that scale across teams and models. Hands-on with Feast, Redis, Kafka, and cloud-native storage backends.
What You'll Learn
This course covers the complete lifecycle of feature store design, from architecture decisions to production deployment.
Offline & Online Stores
Design batch and low-latency serving layers with Parquet, Delta Lake, Redis, and DynamoDB. Implement materialization pipelines.
Real-Time Features
Build streaming feature computation with Kafka and Flink. Implement windowed aggregations with exactly-once semantics.
Governance & Registry
Feature discovery, metadata management, lineage tracking, access control, data quality monitoring, and schema evolution.
Production Patterns
High-availability architecture, cross-region deployment, performance benchmarking, cost optimization, and monitoring.
Course Lessons
Follow the lessons in order for a comprehensive understanding of feature store architecture and implementation.
1. Why Feature Stores Matter
Training/serving skew problem, feature reuse across teams, feature store components, and Feast vs Tecton vs Hopsworks comparison.
2. Offline Feature Store Design
Batch feature computation, storage backends (Parquet, Delta Lake, BigQuery), point-in-time correct joins, and historical feature retrieval.
3. Online Feature Store Design
Low-latency feature serving (<10ms), storage options (Redis, DynamoDB, Bigtable), materialization pipelines, and cache strategies.
4. Real-Time Feature Engineering
Streaming feature computation with Kafka + Flink/Spark Streaming, windowed aggregations, and exactly-once semantics.
5. Feature Registry & Governance
Feature discovery, metadata management, lineage tracking, access control, feature monitoring, and schema evolution.
6. Production Deployment Patterns
High-availability architecture, cross-region deployment, performance benchmarking, cost optimization, and monitoring.
7. Best Practices & Checklist
When to build vs buy, migration strategies, team ownership model, and comprehensive FAQ accordion.
Prerequisites
What you need before starting this course.
- Understanding of machine learning workflows (training, inference, feature engineering)
- Familiarity with Python and SQL
- Basic knowledge of distributed systems concepts (databases, caching, message queues)
- Experience with at least one cloud platform (AWS, GCP, or Azure)