In machine learning or data science projects, code can get messy, quickly.
The lack of good coding habits makes code hard to understand and consequently, modifying code becomes painful and error-prone. This makes it increasingly difficult for data scientists and developers to evolve their ML solutions.
This article shares techniques for identifying bad habits that add to the complexity in code as well as habits that can help us partition complexity.
In interior design, there is a concept (the “Law of Flat Surfaces”) which states that "any flat surface within a home or office tends to accumulate clutter." Jupyter notebooks are the flat surface of the ML world.Read more...