7 Tips to Future-Proof Machine Learning Projects

An Introduction to Developing More Collaborative, Reproducible and Reusable ML Code

7 Tips to Future-Proof ML Project (image by author)

There can be a knowledge gap when transitioning from exploratory Machine Learning projects, typical in research and study, to industry-level projects. This is due to the fact that industry projects generally have three additional goals: collaborative, reproducible, and reusable, which serve the purpose of enhancing business continuity, increasing efficiency and reducing cost. Although I am no way near finding a perfect solution, I would like to document some tips to transform a exploratory, notebook-based ML code to industry-ready project that is designed with more scalability and sustainability.

I have categorized these tips into three key strategies:

• Improvement 1: Modularization — Break Down Code into Smaller Pieces
• Improvement 2: Versioning — Data, Code and Model Versioning
• Improvement 3: Consistency — Consistent Structure and Naming Convention

Improvement 1: Modularization — Break Down Code into Smaller Pieces

Problem Statement

One struggle I have faced is to have only one notebook for…