AI System Architecture Patterns
Batch vs real-time inference, lambda architecture, feature stores, model serving layers.
Beyond the Model
A production AI system is far more than a model file. It includes data pipelines, feature stores, serving infrastructure, and monitoring. Architecture patterns are proven blueprints for assembling these pieces reliably.
Batch vs Real-Time Needs
AI systems often need both:
- Batch processing for accurate, comprehensive results over historical data
- Real-time processing for fresh, low-latency results on new events
Reconciling these two is a recurring design challenge.
All lessons in this course
- AI System Architecture Patterns
- Scalable ML Pipelines with Airflow
- Feature Stores: Feast and Tecton
- AI System Observability and Monitoring