"You've gone from supporting AI projects to being the bottleneck for an AI platform."
📖 This chapter is available in the full book
Six months ago, you provisioned a single GPU VM for the ML team. Today, you have four teams, three AKS clusters, dozens of GPU node pools, and a growing collection of Azure OpenAI endpoints. Each team wants their own resources, their own quotas, and their own SLAs. Your Slack DMs have become a help desk.
"Can you give us more GPUs?" "Why is my training job stuck in Pending?" "Who's using all the A100s?" You're spending more time answering questions than actually engineering anything.
This is the inflection point every infrastructure organization hits. The solution isn't working harder — it's building the systems, policies, and automation that let teams serve themselves while you maintain control.
What You'll Learn in This Chapter
- The Slack Channel That Ate Your Calendar
- From AI Project to AI Platform
- Multi-Tenancy for AI Infrastructure
- GPU Scheduling and Queue Management
- Quota and Capacity Management
- SLA/SLO Design for Inference Endpoints
- Fleet Management
- Observability at Scale
- Self-Service Patterns