mihail911/e2eml-cookiecutter
A generic template for building end-to-end machine learning projects
This helps data scientists and machine learning engineers organize their machine learning projects consistently, from data ingestion to model deployment. It takes a project name and sets up a logical directory structure, creating placeholders for raw data, processed data, notebooks, model checkpoints, and deployment assets. The end result is a well-structured project ready for development, collaboration, and sharing.
No commits in the last 6 months.
Use this if you are starting a new machine learning project and want a standardized, logical structure to keep your work organized and reproducible.
Not ideal if you already have an established project structure you prefer or if you are working on a small, experimental script that doesn't require a full project setup.
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MIT
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Last pushed
Apr 22, 2021
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