Cloud Native Ai Lab

Cloud Native AI Lab


[](https://github.com/peopleforrester/cloud-native-ai-lab/actions/workflows/test.yml) [](LICENSE)

Give a Kubernetes engineer a laptop and seven labs, and watch them schedule GPUs, coordinate distributed training, and serve a model behind an autoscaling API. No GPU required, no cloud bill.

Every lab runs on a local `kind` cluster. If you know pods, deployments, and services, you can start at Lab 00 today. No machine learning background needed.

What you get
7 progressive labs	Kueue, DRA, JobSet, KServe, Gateway API Inference, kagent and MCP
Runs on a laptop	3-node kind cluster, about 8GB RAM, no cloud spend
72 automated tests	version pins, links, manifests, and security context are CI-gated (tests/)
11 project one-pagers	each carrying its own verification date (docs/projects/)

Built as the companion resource to a Cloud Native University talk at [KubeCon + CloudNativeCon Europe 2026](https://events.linuxfoundation.org/kubecon-cloudnativecon-europe/), delivered 24 March 2026 at the RAI in Amsterdam, and maintained since as a standing community resource. The [talk outline](docs/talk-outline.md) is the script as delivered.

Who is this for?

Kubernetes practitioners who are new to AI/ML workloads. If you know pods, deployments, services, and namespaces, you have everything you need to start. This lab does **not** assume any machine learning knowledge.

Learning path

┌─────────┐    ┌──────────┐    ┌─────────┐    ┌──────────┐    ┌──────────┐    ┌─────────────┐    ┌───────────┐
│  Setup  │───▶│  Kueue   │───▶│   DRA   │───▶│  JobSet  │───▶│  KServe  │───▶│ Gateway API │───▶│ kagent +  │
│ La