This is a 3 part series about Continuous Machine Learning. You can check Part II here and Part III here.
What is it?
Continuous Machine Learning (CML) follows the same concept of Continuous Integration and Continuous Delivery (CI/CD), famous concepts in Software Engineering / DevOps, but applied to Machine Learning and Data Science projects.
What is this post about?
I will cover a set of tools that can make your life as a Data Scientist much more interesting. We will use MIIC, a network inference algorithm, to infer the network of a famous dataset (alarm from bnlearn). We will then use (1) git to track our code, (2) DVC to track our dataset, outputs and pipeline, (3) we will use GitHub as a git remote and (4) Google Drive as a DVC remote. I’ve written a tutorial on managing Data Science projects with DVC, so if you’re interested on it open a tab here to check it later.