cookiecutter-data-science and e2eml-cookiecutter
These are competitors offering alternative approaches to standardizing ML project structure, where the established cookiecutter-data-science template provides broader data science workflow organization while the e2eml-cookiecutter template specifically emphasizes end-to-end ML pipeline implementation, requiring a developer to choose one as their project scaffolding foundation.
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 e2eml-cookiecutter
mihail911/e2eml-cookiecutter
A generic template for building end-to-end machine learning projects
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