Machine Learning Times
Machine Learning Times
Six Ways Machine Learning Threatens Social Justice
 Originally published in Big Think When you harness the...
Transitions: Predicting The Next Event
 Models predicting the potential spread of the COVID-19 pandemic...
Coursera’s “Machine Learning for Everyone” Fulfills Unmet Training Requirements
  My new course series on Coursera, Machine Learning...
Segmentation and RFM Analysis in the World of Wine and Spirits
 Segmentation is a hot word these days, and it...

10 months ago
Why Operationalizing Machine Learning Requires a Shrewd Business Perspective

 Originally published in Analytics Magazine For a rocket scientist, the math isn’t the hardest part. What’s hard is being so often misunderstood. The same goes for data scientists, who time and again lack the support needed to successfully launch the fruits of their brilliant labor into action. These math heads have got to integrate into the organization as a whole, lest they vanish into the obscurities of their analysis. Their isolation is an enemy to their usefulness. After all, the most wicked and pervasive pitfall of predictive analytics is organizational in nature, not technical: Predictive models often fail

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