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.
Built on the [cookiecutter](https://cookiecutter.readthedocs.io/) templating framework, it provides a Python package (`ccds` CLI) that scaffolds projects with standardized directories for data pipelines, notebooks, models, and documentation, along with `pyproject.toml` and Makefile conventions for reproducibility. The template separates raw/processed data, intermediate artifacts, and source code into a modular Python package structure with dedicated modules for dataset handling, feature engineering, and model training/inference.
About cookiecutter-ml-research
csinva/cookiecutter-ml-research
A logical, reasonably standardized, but flexible project structure for conducting ml research 🍪
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