Machine Learning Times
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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...
Artifact-Driven Development: Making It Possible to Query Large Analytics and AI Projects
 A practical introduction to making complex project structure explicit...
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5 years ago
Liftoff: The Basics of Predictive Model Deployment

  This article is based on the transcript of one of 142 videos in Eric Siegel’s online course, Machine Learning Leadership and Practice – End-to-End Mastery. Developing a good predictive model with machine learning isn’t the end of the story — you also need to use it. Predictions don’t help unless you do something about them. Your model may be elegant and brilliant, glimmering like the most polished of crystal balls, but displaying it in a report gains you nothing — it just sits there and looks smart. Stagnation be damned — deployment to the rescue! Predictive models

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