Machine Learning Times
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The Data Disconnect: A Key Challenge for Machine Learning Deployment
  EDITOR’S NOTE: This article is excerpted from The...
A Brief History of Why Machine Learning Projects Stall
  EDITOR’S NOTE: This is the preface to The...
Hybrid AI Emerges To Tame LLMs – And Not A Moment Too Soon
 Originally published in Forbes The great potential of LLMs is...
AGI Is Infeasible. Instead, Pursue Superhuman Adaptable Intelligence
  Originally published in Forbes On a recent episode of the...
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By: Dean Abbott, President, Abbott Analytics 

 The Cross Industry Standard Process for Data Mining (CRISP-DM) is the leading published methodology for Data Mining (DM), and by extension, Predictive Analytics (PA). I use it routinely as I lead PA projects and when I teach appiled DM and PA courses. It was the subject of three KD-Nuggets polls, in 2002, 2004, and 2007) and nearly half of the responders stated they used it as the main methodology for DM (http://www.kdnuggets.com/polls/2007/data_mining_methodology.htm). There are six stages in the CRISP-DM process, including Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. There are many excellent books describing five

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