Automated machine learning – or AutoML – was introduced to fill in for the talent gap in the ML industry and eliminate the mundane tasks of ML engineers. Over the years, many AutoML tools have been released. But, how good are these tools? Do they accomplish what they promise? Have they really become a solution to the dearth of talent in the data science industry?

In order to answer these long-standing questions, researchers from Fraunhofer Institute, Germany have investigated the state-of-the-art AutoML frameworks. To their surprise, they found that the AutoML tools are performing better or on par with their human counterparts.

AutoML was introduced to cut down the time spent in doing iterative tasks concerning model development. AutoML tools have helped developers to build scalable models with minimal domain expertise. So, how do they fare when pitted against humans?

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