Lilly Tech Systems
Learn Event-Driven AI
Build real-time AI systems using event-driven architecture — from event sourcing and Kafka streaming to CQRS patterns, real-time ML inference, and scalable AI pipelines that react to events as they happen.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is event-driven AI? Why events are the natural paradigm for real-time ML systems.
2. Event Architecture
Event sourcing, event stores, message brokers, and designing event schemas for AI workloads.
3. Kafka + AI
Build real-time AI pipelines with Apache Kafka: producers, consumers, streams, and ML integration.
4. Real-time ML
Online learning, streaming inference, feature computation, and low-latency prediction serving.
5. CQRS
Command Query Responsibility Segregation for AI: separate read/write models, event sourcing, and projections.
6. Best Practices
Production patterns for reliability, ordering, idempotency, schema evolution, and testing event-driven AI.
What You'll Learn
By the end of this course, you'll be able to:
Design Event Architectures
Architect event-driven AI systems with proper event schemas, message brokers, and processing topologies.
Build with Kafka
Implement real-time AI pipelines using Kafka producers, consumers, Kafka Streams, and connectors.
Deploy Real-time ML
Serve ML predictions in real-time with streaming feature computation and online learning.
Apply CQRS Patterns
Implement CQRS and event sourcing patterns to build scalable, auditable AI systems.