Lilly Tech Systems
Event-Driven Enterprise AI
Build AI systems that react to business events in real time. Learn event architecture for ML, streaming inference, real-time decision engines, and integration patterns for connecting AI with enterprise event streams at scale.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is event-driven AI? Why real-time matters, comparing batch vs event-driven approaches, and key architectural concepts.
2. Event Architecture
Event brokers, schema registries, event sourcing, CQRS patterns, and designing event schemas for ML workloads.
3. Streaming ML
Real-time feature engineering, streaming inference pipelines, windowed aggregations, and online learning on event streams.
4. Real-time Decisions
Decision engines, rule-model hybrids, dynamic pricing, fraud detection, and real-time recommendation systems.
5. Integration
Connecting AI to enterprise systems, CDC from databases, API event bridges, legacy integration, and hybrid architectures.
6. Best Practices
Event schema evolution, exactly-once processing, backpressure handling, testing strategies, and operational excellence.
What You'll Learn
By the end of this course, you'll be able to:
Design Event Architectures
Build event-driven infrastructure with proper schema management, event sourcing, and CQRS patterns for AI workloads.
Build Streaming ML
Create real-time feature engineering pipelines and streaming inference systems that process events as they arrive.
Implement Real-time Decisions
Deploy AI-powered decision engines for fraud detection, dynamic pricing, and personalized recommendations in real time.
Integrate Enterprise Systems
Connect event-driven AI to existing enterprise databases, APIs, and legacy systems with reliable integration patterns.