A six-part walkthrough of building the Ubunye Engine, an open-source, convention-driven machine learning framework. It starts with why convention beats configuration, works through the model registry and the unglamorous reliability work, covers going from a Kaggle-shaped notebook to production, and ends with building alongside an AI agent and putting the whole thing to use.
6 posts
The engine is now on version 0.5.0. This part shows how to actually use it: install it, write your first pipeline, run it on your laptop, then run the exact same folder on Docker, Kubernetes and Databricks. Every snippet here was run before it was written.
This project was built with an AI coding agent as a collaborator throughout. The numbers tell a story that the AI industry mostly avoids having. Here they are.
After all the unit tests and CI pipelines, the question remained: does it actually work on real data? The Kaggle Titanic dataset became the proving ground. This post covers what it proved, what it did not, and how Ubunye compares to existing frameworks.
Documentation, CI/CD, PyPI publishing, and the subtle bugs that live in single lines of code. The thesis is simple: finishing is rarer than starting, and the boring work is what separates a repository from a product.
The model registry was where the project went from interesting framework to something a team could actually use in production. The design principle behind it turned out to be the most significant architectural decision in the entire project.
Every data team eventually hits the same wall. Notebooks work locally. Spark jobs work on the cluster. Nothing works together. This is the problem Ubunye Engine was built to solve.