Lilly Tech Systems
AI Reference Architecture
Master the blueprint for enterprise AI systems. Learn how to design scalable data layers, build robust ML pipelines, architect model serving infrastructure, and apply proven patterns for production-grade AI platforms.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is an AI reference architecture? Why enterprises need standardized blueprints, key principles, and architectural patterns overview.
2. Components
Core architectural components including compute, storage, networking, security layers, and integration points across the AI stack.
3. Data Layer
Data ingestion pipelines, feature stores, data lakes, data quality frameworks, and governance for AI-ready data infrastructure.
4. ML Layer
Model training infrastructure, experiment tracking, model registry, hyperparameter tuning, and distributed training architectures.
5. Serving Layer
Model deployment patterns, inference optimization, A/B testing, canary releases, and scalable serving infrastructure.
6. Best Practices
Production hardening, cost optimization, security patterns, observability, and evolving your architecture over time.
What You'll Learn
By the end of this course, you'll be able to:
Design AI Architectures
Create comprehensive reference architectures that guide enterprise AI system design from data ingestion to model serving.
Build Data Pipelines
Architect scalable data layers with feature stores, data quality checks, and governance controls for ML workloads.
Deploy ML Infrastructure
Set up production-grade ML training and serving infrastructure with proper monitoring and scaling capabilities.
Optimize for Production
Apply best practices for cost management, performance tuning, security hardening, and operational excellence.