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Presented by MinneAnalytics. Hosted by Boston University Questrom School of Business.
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Thursday, August 23 • 2:00pm - 2:45pm
The Benefits and Dangers of Automated Machine Learning

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No-Magic-Wands: Benefits and Dangers of Automated Machine Learning

Automated machine learning promised data scientists a better, faster way to build models. But the reality never matched the hype so far. Most automated machine learning solutions are black boxes that restrict the ability of data scientists to understand how the models work. Putting models like these into production is reckless and sometimes even dangerous. Is this the end of the citizen data scientist then? Not necessarily. But we need a new approach to data science, machine learning, and artificial intelligence. Automated machine learning needs to guide analysts and not overrule their decisions. Novel approaches need to focus on productivity first and on democratization second. But most importantly, they need to deliver reliable models which stop putting organizations or people at risk.

Join Dr. Ingo Mierswa, RapidMiner Founder, for an in-depth discussion on automated machine learning. We will explore:

  • What automated machine learning can do to help accelerate building machine learning models
  • What are the dangers and what could go wrong
  • How the different groups of data scientists can use automated machine learning
  • How to do it right and get most benefits out of automated machine learning

Speakers
avatar for Ingo Mierswa, PhD

Ingo Mierswa, PhD

Founder, RapidMiner


Thursday August 23, 2018 2:00pm - 2:45pm
Room 404 Boston University Questrom School of Business, 595 Commonwealth Avenue Boston

Attendees (57)