A foundational machine learning library designed to streamline the end-to-end process of developing and deploying machine learning models. FundAML offers a broad array of tools and utilities for data preprocessing, model training, evaluation, and deployment, making it a fundamental solution for machine learning tasks.
Project description
fundaml
The purpose of this module is simply to simplify ML learning and use best practices while developing ML models.
Standardization: The fundaml project is an attempt to standardize the way ML projects are structured within a team or organization, promoting best practices for organization, testing, and documentation.
Flexibility: fundaml is designed with flexibility in mind. Users can likely use different parts of the codebase as needed, and extend or modify components to suit their specific requirements.
Extensibility: fundaml could be used as a starting point for building more complex, domain-specific machine learning libraries. The name fundaml itself is intended to be a "base" or foundation upon which other things can be built.
Installation
$ pip install fundaml
from Pypi repo
Developers
Please visit DEVELOPERS.md to set the module for contribution
Usage
See docs
Contributing
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
License
fundaml was created by Tony Zoght. It is licensed under the terms of the MIT license.
Credits
fundaml was created with cookiecutter and the py-pkgs-cookiecutter template.
Code Coverage
Read The Docs
https://fundaml.readthedocs.io/en/latest/?badge=latest
Github Pages
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