要点
End-to-end AI Runway setup on AKS from bare cluster to running model deployment.
- Walks through six sequential steps: cluster verification, controller installation, GPU assessment, inference provider setup, first model deployment, and summary
- Includes cost awareness warnings for GPU node pools and error handling for common deployment failures
- Supports resuming from any step via
skip-to-step Nargument if setup is partially complete - Uses only
kubectlandmakeCLI tools; no MCP tools required
SKILL.md
AI Runway AKS Setup
This skill walks users from a bare Kubernetes cluster to a running AI model deployment. Follow each step in sequence unless the user provides skip-to-step N to resume from a specific phase.
Cost awareness: GPU node pools incur significant compute charges (A100-80GB can cost $3–5+/hr). Confirm the user understands cost implications before provisioning GPU resources.
Prerequisites
This skill assumes an AKS cluster already exists. If the user does not have a cluster, hand off to the azure-kubernetes skill first to provision one (with a GPU node pool unless CPU-only inference is acceptable), then return here.
Quick Reference
| Property | Value |
|---|---|
| Best for | End-to-end AI Runway onboarding on AKS |
| CLI tools | kubectl, make, curl |
| MCP tools | None |
| Related skills | azure-kubernetes (cluster setup), azure-diagnostics (baseline troubleshooting), optional aks-gpu-inference (existing GPU/inference Day-2 incidents only) |