kubernetes-for-ml-engineers

Why we included this project

Kubernetes appears in almost every ML engineer job posting, but it's one of the harder parts of the stack to get comfortable with. This repo walks you through the basics end to end: you install the tools, spin up a local cluster with kind, containerize a small FastAPI app with Docker, and deploy it as a service. The author keeps the scope tight on purpose, and takes time to explain the reasoning behind each step, like why a local cluster is enough for development and CI while production clusters usually live in a cloud provider and are managed for you. If Kubernetes has felt like a wall of YAML and unfamiliar concepts, this is a short, practical path to building the mental model you need to deploy your own services for real.