DS repository structures and frameworks

• Tags:: 📝CuratedNotes , Data methodology, Scalable computing of features

Favorite refs

My favorite so far: https://towardsdatascience.com/how-to-structure-a-data-science-project-for-readability-and-transparency-360c6716800

On repository structure, we have the typical:

• Home - Cookiecutter Data Science

But there are others such as:

• dslp/dslp: The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo. (github.com)

Close to this, going up in complexity (based on DAGs)

• Kedro spaceflights tutorial — Kedro 0.17.7 documentation
• Hamilton: Scaling to Match your Data! | Stitch Fix Technology – Multithreaded

A full framework: Why Metaflow | Metaflow Docs

Also, a good summary on how to share features: How Machine Learning Teams Share and Reuse Features - Tecton