By Scott Matteson


Machine learning (ML) remains an area of strong investment these days as businesses seek to automate operations via intelligent mechanisms, which can adjust and adapt as needed. This reduces the need for human intervention—provided the right series of controls are in place.


However, there is no one-size-fits-all approach to adopting machine learning; most companies and the departments therein approach the concept from different perspectives with an array of various objectives. Some of these objectives are more coherent than others, which produces inefficiencies and unexpected outcomes, and may eventually cause a machine learning shakeout.


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