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Seven Questions to Ask before Introducing AI into Your Project

AKA First rule of machine learning? Don’t use machine learning.

Aliaksei Mikhailiuk
Towards Data Science
7 min readNov 5, 2021

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With all the buzz around the success of machine learning in finding new drugs and empowering self-driving cars the expectation is that it is available and very affordable. If it can solve those big problems it must be easy to apply to something smaller.

I remember the excitement during my undergrad when I first learned about CNNs. In young boisterous minds all problems had a solution and this solution was deep neural networks. Experience taught me otherwise — if not used properly, machine learning algorithms turn into a problem themselves.

While writing the article I stumbled upon this LinkedIn post drawing the difference between two machine learning teams approaching the same problem — one rushing to bring a machine learning solution at all costs and another putting time into understanding the problem. I am a much bigger fan of the second approach.

This article is a summary of areas that I pay attention to while deciding whether to go ahead with a machine learning solution. With all that said, let’s dive in!

Problem

Is the problem suitable for machine learning?

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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Aliaksei Mikhailiuk
Aliaksei Mikhailiuk

Written by Aliaksei Mikhailiuk

Tech Lead Manager at Snap. Ex-AI Team Lead at Huawei, PhD from University of Cambridge https://www.linkedin.com/in/aliakseimikhailiuk/

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