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

Dr. Ingo Mierswa, RapidMiner

Automated machine learning promised data scientists a better, faster way to build models. But the reality hasn’t 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.


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