# mlops

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 job hasn't disappeared. The mental model has changed. Here's what the roadmap looks like for data scientists and engineers entering (or navigating) the AI agent era, with a South African lens.

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.