Ep 123: Monitoring in Production — When Models Go Wrong | LLM Mastery Podcast

Description

Here's what you need to know about monitoring in production — when models go wrong: - LLM systems fail silently — degradation is gradual and invisible without active monitoring, making observability essential from day one - Monitor five pillars: quality, latency, cost, errors, and safety — missing any one creates a blind spot that will eventually bite you - Data drift and model drift are the two primary causes of production degradation, and both require different detection strategies - Human review of production samples catches qualitative issues that no automated metric can detect — build it into your team's weekly routine - Have a pre-built incident response playbook with rollback mechanisms ready before you need them Up next: With deployment and monitoring covered, we are ready to start building real applications. Tomorrow we begin the Building Apps module by constructing a text generation application from scratch — from choosing an API to handling streaming responses to deploying the finished product. --- Series: LLM Mastery Podcast | Module: Foundations 138 episodes taking you from zero to production with LLMs. #AI #LLM #MachineLearning #Podcast #SoftwareEngineering #Foundations Carlos Hernandez | roclas.com