ml-project-template

A starter layout for machine learning projects that argues notebooks stop scaling, drawing on lessons from more than ten industrial ML projects.

The README starts with a familiar scene. Most ML practitioners begin with notebooks and a couple of folders, and it works, until the project grows. Then reproducibility breaks, pipelines get messy, and deploying models turns into chaos. The repository is the counterproposal, a template for structuring an ML project, written in Python and carrying no license in the metadata. The audience is anyone who has watched a research experiment turn into something that has to be shipped.

The pitch is to organize an ML project like a professional software system, which the README spells out as modular, reproducible, and production ready. The structure is said to be based on lessons learned from more than ten industrial-level ML projects, which is the closest the docs come to credentials. The goal is a project that stays maintainable as it grows past the notebook stage, with the template offered as a starting point rather than a finished answer.

The README lists the forces that make structure matter: multiple versions of datasets, experimental code evolving into production workflows, model artifacts that must be versioned and tracked, and complex pipelines from raw data to predictions. Each of those breaks down without a deliberate layout. The template's claim is that planning for them up front keeps the project maintainable, reproducible, and ready for production.

This is a philosophy document more than a code release, a template whose real content is the argument for treating an ML project like software that will outlive its first experiment.

• Source 1 · Project repository
• Source 2 · README