Automatically Set Up a New ML Project, Pain Free

The article discusses a template created by Tarmily Wen, Principal Computer Vision Research Engineer at ADT, using cookiecutter to automate the setup process for each new machine learning (ML) project. This approach presents several benefits including consistency, reproducibility, data standardization, and speed. The python ML project cookiecutter template adds essential features such as environment reproducibility, code consistency, and data standardization, along with some "nice to haves" like documentation generation and test automation to every ML project. It uses docker for a consistent environment inside of the container, poetry for consistent Python package dependencies, and FiftyOne for data visualization and formatting. The template also provides continuous integration (CI) using GitHub Actions to maintain code quality.