Data Science

Okay, You’ve Trained the Best Machine Learning Model. What’s Next?

An MLOps project beyond modeling in Jupyter Notebook

Photo by Elena Mozhvilo on Unsplash

Table of Contents
· Initialize a Repository
· Migrate Your Codebase
  ∘ config/config.py
  ∘ config/args.json
  ∘ tagolym/utils.py
  ∘ tagolym/data.py
  ∘ tagolym/train.py
  ∘ tagolym/predict.py
  ∘ tagolym/evaluate.py
  ∘ tagolym/main.py
· Package Your Codebase
· Setup Data Source Credential
· Run Your Pipeline
· Miscellaneous
· Push Your Project to GitHub
· Wrapping Up

Let’s say you’re building a data science project, maybe for work, college, portfolio, hobby, or whatever it is. You’ve spent your days solving a problem statement and experimenting with Jupyter notebooks. Now, you’re wondering, “How do I deploy my work as a useful product?”.

To be concrete, assume you have a website that hosts forums. Users can add tags to a thread in a forum to ease navigating between forums with different topics. You want to better the user experience by suggesting predefined tags hence giving context to what the discussion is about.

The forum can be anything, so let’s be more specific; it always starts with a post explaining a math problem, followed by thoughts, questions, hints, or answers around it. Below is what a thread looks like…