3 reasons why machine learning projects fail - and how to avoid them

By secondmind - 2020-11-24

Description

When push comes to shove, many AI projects either fail to scale, are put on hold or simply never materialize. How come?

Summary

Authors Patrick White, Gaurav Bajaj There’s no denying the competitive edge and the value promise that AI has to offer: It is a common expectation that you can buy an AI solution off the shelf, plug it in, and away you go.
You can never replace the experience that your people possess.
get out of the box “Set-up a central AI program and invest in deploying solutions that are forward looking and that scale for value.

Topics

Management (0.22)
Machine_Learning (0.19)
Backend (0.09)

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