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Building a machine learning (ML) model is exciting. Arriving, after tweaks and changes and sleepless nights, at something you feel you can use in the real world is likely a relief. Unfortunately, that proof of concept you’ve just created marks the beginning of your work, not the end. After establishing the baseline model, it’s time to build a more thorough version, test, deploy, and maintain that model. The work of an ML team is never finished, it seems. Here are some considerations as your team turns its proof of concept into the real deal: a model that is the engine of a real-world application. If we think about an object-detection model for pedestrians, we can walk through the various steps required to turn the concept into production. Before we get into the nitty-gritty details, teams will have to agree upon a few basics.

How to Build, Train, Test, and Deploy a Machine Learning Model