I intend to use this tool over the course of my honours project, making it easier to iterate on the data science experimental cycles, and compare different approaches to solve the same problem.

Provide a UI to kick off background jobs using Faktory, which are processed by a Faktory worker in Python3.

Provide an API which wraps python libraries such as numpy, scikit-learn, pandas, keras, pytorch to implement the standard ML workflow. While this ML tooling is still being developed (and my honours project hasnt technically started yet), I will spend time looking into other people's ML projects, and try to extract meaningful functionality into reusable parts. Also this will give me a nice way to get exposed and actively research other people's projects.

The python component of this project relies on the packages in requirements.txt, direnv for automatic environment switching, and pyenv for managing python versions.