Module 04 of 6~25 minAccount
Production AI Systems
Monitoring, error handling, cost optimization, and reliability at scale.
§ You will learn
- Define what 'production-ready' means for an AI system and identify the gaps between a prototype and a reliable deployment
- Design a monitoring strategy that catches output quality degradation, latency regressions, and cost anomalies before they become incidents
- Implement layered error handling including retries with backoff, fallback models, and human-in-the-loop escalation paths
- Apply concrete cost optimization techniques (semantic caching, model routing, and prompt compression) to reduce spend without sacrificing quality
- Build an evaluation framework that tests AI behavior continuously, catches regressions, and gives you confidence when deploying changes
- Add error handling, cost tracking, and an evaluation suite to an existing AI system
§ Sealed entry
This module is on file for account holders.
6 sections · ~25 min · objectives above are the preview