Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
How Machine Learning Works for Social Good
  Originally published in KDnuggets, Nov 2020. This article...
Diversity and Collaborative Problem Solving to Address Wicked Data Ethics Problems
 The complexity of the ethical issues facing professionals who...
Climate Tech Needs Machine Learning, Says PAW Climate Conference Chair
  Straight from the horse’s mouth – the founding...
Predictive Policing: Six Ethical Predicaments
  Originally published in KDNuggets. This article is based...
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1 year 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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