Over the last decade, machine learning underwent a broad democratization. Countless tutorials, books, lectures, and blog articles have been published related to the topic. While the technical aspects of how to build and optimize models are well documented, very few resources are available on how developing machine learning models fits within a business context. When is it a good idea to use machine learning? How to get started? How to update a model over time without breaking the product?
Below, we’ll share five steps and supporting tips on approaching machine learning from a business perspective. We’ve used these steps and tips at Shopify to help build and scale our suite of machine learning products. They may look simple, but when used together they give a straight-forward workflow to help you productionize models that actually drive impact.
Before starting the development of any machine learning model, the first question to ask is: should I invest resources in a machine learning model at this time? It’s tempting to spend lots of time on a flashy machine learning algorithm. This is especially true if the model is intended to power a product that is supposed to be “smart”. Below are two simple questions to assess whether it’s the right time to develop a machine learning model: