cookiecutter-data-science and cookiecutter-ml-research
These are competitors serving overlapping use cases—both provide cookiecutter templates for structuring ML/data science projects—though A is vastly more mature and widely adopted while B is specialized for research workflows.
About cookiecutter-data-science
drivendataorg/cookiecutter-data-science
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Setting up a data science project can be complex, with many files and folders to organize. This tool helps data scientists quickly create a standardized, logical structure for new projects, providing a consistent layout for raw data, processed data, notebooks, models, and reports right from the start. It ensures all team members can easily understand and navigate the project's layout.
About cookiecutter-ml-research
csinva/cookiecutter-ml-research
A logical, reasonably standardized, but flexible project structure for conducting ml research 🍪
This project provides a standardized framework for machine learning researchers to organize their algorithm design projects. It helps manage the iterative process of developing new ML algorithms, defining their architectures, and systematically comparing them against baselines across various hyperparameters. The output is well-structured code, experimental results, and analysis notebooks, primarily for data scientists and ML researchers.
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