Machine Learning projects often die, because of poor feature economics.
Why? ML is expensive to build, relative to software.
It’s a lot like carbon fiber…
Highly performant, but expensive.
When teams use ML for use-cases that don’t require ML, they take on extra (and unnecessary) expense that hurts their feature economics.
So, try this instead.
Next time you have a potential ML use case, see if there’s a way to solve the problem with either:
1) a non-ML solution
2) or a very simple ML approach
Start there. See if it works. If more complexity and performance is needed, then add more ML sophistication.
But do it, because you’ve proven it’s needed, not because you can.
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