Chapter 12 — The Production Troubleshooting Playbook Full Book
"These aren't hypothetical. They're distilled from hundreds of production incidents."
📖 This chapter is available in the full book
This chapter is organized as a collection of real-world failure scenarios — the ones that generate 2 AM pages, derail sprint demos, and make you question your career choices.
These aren't hypothetical. They're distilled from hundreds of production incidents across GPU infrastructure, Kubernetes AI workloads, and Azure OpenAI deployments. Some you'll hit on your first day. Others will ambush you six months in, right when you think everything is stable.
Read through them once to build pattern recognition. Then keep this chapter bookmarked — you'll come back to it.
What You'll Learn in This Chapter
- Scenario 1: NVIDIA Driver Crash After Kernel Update
- Scenario 2: CUDA Out of Memory During Fine-Tuning
- Scenario 3: AKS GPU Pods Stuck in Pending
- Scenario 4: Azure OpenAI 429 Storm
- Scenario 5: Inference Latency Spike
- Scenario 6: Distributed Training Hangs at Gradient Sync
- Scenario 7: Model Serving Container Crash Loop
- Scenario 8: GPU Quota Exhaustion
- Scenario 9: BlobFuse2 Mount Failures
- Scenario 10: Model Quality Degradation in Production
- Quick Reference: Diagnostic Command Cheatsheet