Myth of the Machine Learning Black Box

Was unable to get to see this, but of interest.  Most all machine learning projects need to be updated,  maintained and reevaluated.   Maybe they are not black boxes .... but are effectively so, because the decision makers have to do a considerable amount of work to figure them out.

The Myth of the Machine Learning Black Box
Added by Tim Matteson on June 21, 2017 

Critics describe machine learning as a "black box," where data goes in and a prediction comes out, without visibility into how the prediction was derived. This lack of transparency makes it difficult to evaluate and update predictive models as conditions change or new sources of data become available. But today's machine learning systems are not black boxes, allowing data scientists and business professionals alike to understand how a model makes its predictions.

In this Data Science Central webinar, DataRobot will discuss how today's automated machine learning systems provide the information and visualizations that deliver deep insights that break out of the black box.

Speaker: Greg Michaelson, Director of DataRobot Labs -- DataRobot

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central