What is kserve-k8s?

This repository is a minimal, working example of running KServe, a tool for serving machine learning models on Kubernetes, the popular system for running containerized applications at scale. Normally, setting up all the pieces needed to serve a model, like the server itself, networking, and automatic scaling, takes a lot of manual configuration. KServe removes most of that by letting you describe a model in one small file, called an InferenceService, and it automatically creates everything needed behind the scenes. This repo walks through that exact flow using a tiny example model built with scikit learn, a common machine learning library. It includes an install script that sets up the required tools, a single configuration file describing the model to deploy, and a test script that sends a sample prediction request to confirm everything works. Running it is a short sequence of commands: install the required pieces, apply the model configuration, wait for it to become ready, then run the test script, which should return a small prediction result confirming the model responded correctly. Once running, you can also inspect the ordinary Kubernetes objects KServe generated automatically, like the deployment, the network service, and the autoscaling rule, since debugging problems usually means looking at these generated pieces rather than the InferenceService description itself. The setup deliberately uses a simpler deployment mode that avoids extra dependencies, though this means it cannot scale a model down to zero when unused, a feature only available in KServe's more complex mode. This project is aimed at developers or machine learning engineers who want a small, working reference for deploying a model on Kubernetes with KServe before attempting it with a real model.