It seems to be getting harder to motivate data scientists to work on simple models like binary classifiers, even though they're often the most valuabl

A checklist for professionalizing machine learning models

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2021-05-27 22:00:08

It seems to be getting harder to motivate data scientists to work on simple models like binary classifiers, even though they're often the most valuable and reliable machine learning assets. As machine learning software has matured and idioms have stabilized, creating basic tabular supervised models has become somewhat rote. But rather than chasing the latest shiny ML headlines, we should take more pride in professionalizing the models we know bring value to our organizations.

I use the term professional in the sense of a job performed at a high standard of quality and completeness, and for meaningful stakes. Not all models created by a professional data scientist reach this level; most are experimental and never see the light of day—that's normal and fine.

Model professionalization is one part of productionization.1 In addition to the items listed below—which are usually owned by a data scientist—productionization involves engineering components like data pipelines, containerization, serving at scale, and dev ops.

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