Check out this answer by Chinasa T. Okolo on TwoCents

Courage Ekoh

Kigali • June 20, 2021, 7:32 p.m.

Over the last couple of years, ML modelling skills have become ubiquitous such that anyone can literally create a regression model, or some classification model (i.e, model.fit()). However, I feel the needs in the industry are broader. In recent years we have heard about model deployment alot. I'd like it if you can talk about relevant knowledge and skills that make the difference in the industry. Secondly, for someone in a backgroud in Engineering say Operations Research, what are some research areas in Applied ML one can look into for a PhD, that may eliminate some of the more mathematical aspects of ML and focus more on Application?

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Chinasa T. Okolo Managing Director & Com... @ The Diasporic Gro...

New York City, United States • June 30, 2021, 3:31 p.m.

Industry is very broad since there are so many different applications large tech companies are working on. The needs are pretty much the same, it just depends on what specific project it is (ads targeting, email autocompletion, heart rate detection, etc.). I would say the major difference is learning how to scale these algorithms for use across thousands, maybe millions of users but this has pretty much been figured out.

Pretty much everything out of theoretical ML is applied, so if you're interested in working with text data pursue projects in NLP or if you like image + video data, pursue Computer Vision!

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