Production LLM Pipelines — From Prototype to Scale

Take your LLM application from a Jupyter notebook to a production system serving thousands of users. Covers observability, caching, failover, cost management, and CI/CD for AI.

Lessons

1. The Production Readiness Gap — What breaks when you go from demo to real users (+70 XP)
2. Observability for LLM Apps — Logging, tracing, and monitoring AI-specific metrics (+90 XP)
3. Intelligent Caching Strategies — Semantic caching, TTL policies, and cache invalidation (+80 XP)
4. Failover & Fallback Patterns — Multi-provider routing and graceful degradation (+90 XP)
5. Cost Management at Scale — Token budgets, model routing, and spend alerts (+80 XP)
6. CI/CD for AI Applications — Prompt regression testing and automated eval pipelines (+90 XP)
7. Load Testing & Auto-Scaling — Handle traffic spikes without breaking the bank (+80 XP)
8. Security Hardening — API key rotation, rate limiting, and audit logging (+70 XP)