Lilly Tech Systems
AI System Design Fundamentals
Learn the architecture patterns that separate production AI systems from Jupyter notebook prototypes. This course covers the real decisions you face when building AI/ML systems — data pipelines, model serving, scaling GPU inference, handling failures gracefully, and keeping costs under control.
Your Learning Path
Follow these lessons in order for a complete understanding of AI system design, or jump to any topic that interests you.
1. How AI Systems Differ
Why AI systems are fundamentally different from traditional software: non-deterministic outputs, data-dependent behavior, GPU costs, and the key components every production AI system needs.
2. Requirements Analysis
Latency budgets (p50/p95/p99), throughput estimation with real QPS numbers, data volume calculations, GPU cost constraints, and a complete AI system requirements document template.
3. Data Architecture Patterns
Lambda vs Kappa for ML, feature store design (online/offline), data lake vs warehouse trade-offs, real-time vs batch pipelines, and data versioning with DVC and Delta Lake.
4. Model Serving Architecture
Sync vs async inference, TorchServe/Triton/vLLM comparison, canary deployments for models, multi-model routing, GPU auto-scaling, and production Kubernetes YAML.
5. Scaling AI Systems
Horizontal vs vertical GPU scaling, caching strategies (prompt/embedding/result), request batching for throughput, model vs data parallelism, and real cost/latency numbers at scale.
6. Reliability & Fault Tolerance
Graceful degradation when models fail, fallback chains (simpler model → rules → cache), circuit breakers for AI services, health checks, and SLA design for non-deterministic systems.
7. Cost-Aware AI Architecture
GPU cost modeling (on-demand vs spot vs reserved), inference optimization (quantization, distillation, caching), build vs buy analysis, multi-tier serving, and real cost comparison tables.
8. Design Checklist & Best Practices
Complete AI system design checklist from requirements to monitoring, common mistakes and how to avoid them, architecture review template, team structure, and FAQ with real-world answers.
What You'll Learn
By the end of this course, you will be able to:
Design Production AI Systems
Architect end-to-end AI systems with proper data pipelines, model serving infrastructure, monitoring, and feedback loops — not just train models in notebooks.
Make Architecture Decisions
Choose between sync vs async inference, batch vs real-time pipelines, feature stores vs direct queries, and GPU vs CPU serving based on real requirements.
Control AI Infrastructure Costs
Model GPU costs accurately, optimize inference spend with quantization and caching, and design multi-tier architectures that route cheap queries to cheap models.
Build Reliable AI Services
Implement graceful degradation, circuit breakers, fallback chains, and health checks that keep your AI services running even when individual models fail.