Always Measure Your Baseline — A Golden Rule for Data Scientists

You can easily find yourself set up for failure. Verifying and checking metrics before you start was one of the most valuable lessons I learned (the hard way).

Before starting any project to improve an existing model or process, be sure to check you’re not being measured unfairly (photo by William Warby on Unsplash)

Destined for failure

This is an issue that I’ve seen a few times over the years and have run afoul of early in my career. It’s also one of the most important lessons I ever learned.

It’s common knowledge that a large proportion of data science projects fail. This is often attributed to working models never making it into production and the difficulties around MLOPs and machine learning engineering. Before I continue, if you’re wanting a super simple guide to walking through the end-to-end process I wrote a post on this a while back that goes through the basics. You can check out here:

Docker for data scientists-Part 2

It’s not going to land you an MLE role any time soon but if you’re just starting out it can really boost your confidence and demystify some…