Chapter 7 — Monitoring and Observability for AI Workloads Full Book
"You're staring at a wall of healthy metrics while the system is actively failing your users."
📖 This chapter is available in the full book
Your Azure OpenAI endpoint is returning 200 OK on every request. Latency looks normal — P95 is under 800ms. CPU and memory utilization are well within thresholds. By every infrastructure metric you've ever trusted, the system is perfectly fine.
But the support tickets keep coming. Users are reporting that the chatbot is "giving worse answers." The responses are technically fluent but factually wrong — hallucinations have increased, summaries miss key points, and code suggestions introduce subtle bugs.
You pull up your monitoring stack. Azure Monitor: green. Application Insights: green. Grafana dashboards: all green. You're staring at a wall of healthy metrics while the system is actively failing your users.
What You'll Learn in This Chapter
- The Silent Failure
- The Six Dimensions of AI Observability
- GPU Monitoring Deep Dive
- Azure OpenAI Monitoring
- Application-Level Observability
- KQL Queries for AI Troubleshooting
- Alerting Strategy
- Dashboards That Tell a Story
- Hands-On: Set Up GPU Monitoring with Prometheus and Grafana