Lilly Tech Systems
ML Feature Platform at Scale
Master the design, implementation, and operation of enterprise-grade feature platforms — from architecture and online/offline serving to feature computation, monitoring, 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 platform? Why centralized feature management matters for ML at enterprise scale.
2. Architecture
Reference architectures for feature platforms: storage layers, compute engines, APIs, and metadata management.
3. Online/Offline
Online stores for real-time serving, offline stores for training, materialization strategies, and dual-write patterns.
4. Computation
Feature computation engines, batch vs. streaming transforms, scheduling, and incremental processing.
5. Monitoring
Feature quality monitoring, drift detection, freshness SLAs, alerting pipelines, and observability dashboards.
6. Best Practices
Naming conventions, governance, cost optimization, team workflows, and production operations at scale.
What You'll Learn
By the end of this course, you'll be able to:
Platform Architecture
Design scalable feature platform architectures with online/offline stores, compute engines, and metadata layers.
Feature Computation
Build batch and streaming feature pipelines with proper scheduling, backfills, and incremental updates.
Monitoring & Observability
Implement feature quality monitoring, drift detection, and freshness alerting for production reliability.
Scale Operations
Operate feature platforms across teams with governance, cost management, and organizational best practices.