Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Survey: Machine Learning Projects Still Routinely Fail to Deploy
 Originally published in KDnuggets. Eric Siegel highlights the chronic...
Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...
Getting Machine Learning Projects from Idea to Execution
 Originally published in Harvard Business Review Machine learning might...
Eric Siegel on Bloomberg Businessweek
  Listen to Eric Siegel, former Columbia University Professor,...

Original Content

Ghosts in the Data, Constructing Data Entities

 Data Entities are seldom discussed concepts that primarily hide in the shadows or are ghosts on the periphery. These entities are data constructs that are observationally defined in terms of the underlying data set that can serve as the business level aggregation. It is common in many data sets to use identifier fields to define

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Dr. Matteo Bellucci at General Electric

 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference keynote presentation, Changing the Way we Make Things: The Brilliant Factory, we interviewed Dr. Matteo Bellucci, Manager, Process System Lab at General Electric. View the Q-and-A below for a glimpse...

HR’s First Predictive Project? Pre-hire Candidate Screening

 Corp recruiters have a very important and difficult job. They predict who will be a top performer in certain roles and protect against non-performers getting inside the business ecosystem. We rely on their ability to make constant...

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Gary Neights at Elemica

 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Analytics in Manufacturing Supply Chains – Predicting Behavior In Chemical Industry Supply Chains, we interviewed Gary Neights, Senior Director at Elemica. View the Q-and-A below for a glimpse of...

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Jeffrey Banks at The Applied Research Laboratory at The Pennsylvania State University

 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Using Vehicle Digital Bus Data for Predicting Failure of Line Haul Trucks, we interviewed Jeffrey Banks, Department Head, Complex Systems Engineering & Monitoring at The Applied Research Laboratory...

Wise Practitioner – Text Analytics Interview Series: Frédérick Guillot at Co-operators General Insurance Company

 In anticipation of his upcoming conference presentation, Leveraging Hands on Approaches to Identify Actionable Topics in Property Insurance at Text Analytics World Chicago, June 21-22, 2016, we asked Frédérick Guillot, Manager, Research and Innovation at Co-operators General...

Wise Practitioner – Predictive Analytics Interview Series: Alice Chung at Genentech

 In anticipation of her upcoming conference co-presentation, Utilizing Advanced Analytics to Generate Insights at Predictive Analytics World Chicago, June 20-23, 2016, we asked Alice Chung, Senior Manager at Genentech, a few questions about her work in predictive...

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Carlos Cunha at Robert Bosch, LLC

 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Manufacturing Analytics at Scale:  Data Mining and Machine Learning inside Bosch, we interviewed Carlos Cunha, Senior Data Scientist at Robert Bosch, LLC. View the Q-and-A below to see...

5 Common Mistakes Multi-Channel Retailers Make, and How to Avoid Them

  Multi-channel retailers are often finding themselves stuck in a vicious cycle of failed promotions, inventory distortion, and endless markdowns which result in decreasing gross margins, and lost sales. Yet all these challenges have only a handful...

Three Critical Definitions You Need Before Building Your First Predictive Model

 Portions excerpted from Chapter 2 of his book Applied Predictive Analytics (Wiley 2014, http://amzn.com/1118727967) Successful predictive modeling is more than identifying the right algorithms. And, even though 60-90% of our time is spend on data preparation before...

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