In my post Research guidelines for Machine Learning project, I explained how to split any Machine Learning projects into two stages (Research and Development) and some tricks to boost the Research stage.

In this post, I will delve into some techniques and tools that will help you out mastering your Research. While working in this stage, you should strive for simplicity and focus.

---

Project layout

This is the folder layout I tend to use at the beginning of any ML project. This layout is open to extension (such as adding a tests folder, deploy folder, etc) as soon as the project needs to grow up.

project          ## project root
├── data         ## data files
├── models       ## machine learning models
├── notebooks    ## notebook files
└── src          ## helper functions

Unlike regular software development projects, ML projects have 3 foundational stones: the source code (notebooks and src), the data consumed/produced by the code, and the model built/consumed by the code and the data.

#data-preprocessing #wsl #profiling #jupyter-notebook #machine-learning