The Kubeflow Blog

This is a blog website for Kubeflow, a tool that helps machine learning engineers manage and run experiments on distributed computing systems. The blog publishes articles, tutorials, and case studies about using Kubeflow and best practices in machine learning operations.

The blog is built on fastpages, a framework that simplifies blogging by letting writers submit content in two formats: markdown (plain text with simple formatting) or Jupyter notebooks (the interactive coding environments data scientists already use). This means an engineer can write a blog post in the same notebook where they experimented with their code, then publish it directly without needing to learn web design or reformat their work.

To contribute, writers propose their article idea by opening an issue, write the post, test it locally to make sure it looks right, and then submit it as a pull request. The Kubeflow team then reviews it in two stages: first, subject matter experts from the relevant technical work group review the content for accuracy, and then an approver gives final sign-off before it goes live on blog.kubeflow.org.

The README emphasizes that all contributors should follow Kubeflow's standard contributing guidelines and points to the fastpages documentation for help with technical questions about how the blogging platform works. The lightweight submission process is designed to make it easy for people working with Kubeflow to share what they've learned without friction.