cookiecutter-data-science and cookiecutter-docker-science
These are complementary tools: cookiecutter-data-science provides the project structure and organization, while cookiecutter-docker-science adds containerization capabilities that can wrap around that structure for reproducible deployment.
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-docker-science
docker-science/cookiecutter-docker-science
Cookiecutter template for data scientists working with Docker containers
This project helps data scientists set up and manage their machine learning experiments to ensure results are consistent and reproducible. It takes your project name and data source, then generates a structured project directory with pre-configured Docker settings and useful scripts. Anyone running machine learning or data mining experiments who struggles with inconsistent environments will find this helpful.
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