The Dr. Data Show with Eric Siegel

The Dr. Data Show with Eric Siegel

Eric Siegel covers why machine learning is the most important, most potent, and most misunderstood technology. And did I mention most important? Yup, it’s the most important – yet most new ML projects fail to deliver value. This podcast will help you: - Make sure machine learning is effective and valuable - Catch common machine learning oversights - Understand ethical pitfalls – concretely - Sniff out all the ”artificial intelligence” malarky This podcast is for both data scientists and business leaders of all kinds – such as executives, directors, line of business managers, and consultants – who are involved in or affected by the deployment of machine learning. To get machine learning to work, both the tech and business sides must make an effort to reach across wide chasm. About the host: Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling ”Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die,” which has been used in courses at hundreds of universities, as well as ”The AI Playbook: Mastering the Rare Art of Machine Learning Deployment.” Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate *computer science* courses in ML and AI. Later, he served as a *business school* professor at UVA Darden. Eric has appeared on numerous media channels, including Bloomberg, National Geographic, and NPR, and has published in Newsweek, HBR, SciAm blog, WaPo, WSJ, and more.


February 13, 2024 18 mins

This episode covers five insights from the new book, The AI Playbook, which come from a piece originally published by The Next Big Idea Club.

On a related note, the book has been included as a Next Big Idea Club Must Read.

Also, here is the book's recent Bloomberg Businessweek Radio segment, which was mentioned herein.

For more about The AI Playbook, see the details, endorsements, and ordering options at

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I'm excited to announce that today, my new book has published!

The AI Playbook: Mastering the Rare Art of Machine Learning Deployment

Info at:

In my first book, Predictive Analytics, I explained how machine learning works. Now, in The AI Playbook, I show how to capitalize on ML. The book presents a greatly-needed business framework that I call bizML.

See all the details, recommendations from the likes of Scott ...

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In this episode, Eric Siegel narrates his article in The Harvard Business Review, "The AI Hype Cycle Is Distracting Companies."

Access the original article here:

Learn more about and order Eric's new book, The AI Playbook:

Machine learning has an “AI” problem. With new breathtaking capabilities from generative AI released every several months — ...

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In this episode, Eric narrates his new Harvard Business Review article, "Getting Machine Learning Projects from Idea to Execution," adapted from his new book, The AI Playbook.

Access the article:

Learn more about and order The AI Playbook:


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Announcing Eric Siegel's new book:

The AI Playbook: Mastering the Rare Art of Machine Learning Deployment


This podcast episode includes a book overview, book sample – the opening of the book's Introduction – and a free audiobook offer.

In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it...

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In this special episode, rather than the usual conceptual coverage of machine learning, Eric Siegel will pitch you on the machine learning conference series he founded in 2009, the leading cross-vendor, cross-industry event covering the commercial deployment of machine learning and predictive analytics.

Join him in Las Vegas June 19-24 for Machine Learning Week 2022, with seven tracks of sessions covering the commercial deployment ...

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When it comes to deploying machine learning, we must learn from the self-driving car movement – both to gain inspiration as to what it takes and as a major cautionary tale as to what mistakes to avoid. This episode covers four things the entire machine learning industry must learn from the self-driving car movement.

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Deep learning, the most important advancement in machine learning, could inadvertently expedite the next AI winter. The problem is that, although it increases value and capabilities, it may also be having the effect of increasing hype even more. This episode covers four reasons deep learning increases the hype-to-value ratio of machine learning.

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“An orange used car is least likely to be a lemon.” At least that’s what was claimed by The Seattle Times, The Huffington Post, The New York Times, NPR, and The Wall Street Journal. However, this discovery has since been debunked as inconclusive. As data gets bigger, so does a common pitfall in the application of standard stats: Testing many predictors means taking many small risks of being fooled by randomness, adding up to one bi...

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Organizations often miss the greatest opportunities that machine learning has to offer because tapping them requires real-time predictive scoring. In order to optimize the very largest-scale processes – which is a vital endeavor for your business – predictive scoring must take place right at the moment of each and every interaction.

The good news is that you probably already have the hardware to handle this endeavor: the same syste...

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Misleading headlines abound, claiming that machine learning can "accurately" predict criminality, psychosis, sexual orientation, and bestselling books. But, when practitioners claim their model achieves "high accuracy," it's often bogus. Can AI "tell" if you're going to have a heart attack? Contrary to bold, public claims, no it cannot. This episode unpacks the undeniable yet common "accuracy fallacy," which misleads the public int...

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Our latest industry poll reconfirms today's dire industry buzz: Very few machine learning models actually get deployed. In this episode, I summarize the poll results and argue that this pervasive failure of machine learning projects comes from a lack of prudent leadership. I also argue that MLops is not the fundamental missing ingredient – instead, an effective machine learning leadership practice must be the dog that wags the mode...

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Eric Siegel covers why machine learning is the most important, most potent, most screwed up, most misunderstood, and most dangerous technology. And did I mention most important?

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