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Will AutoML Be the End of Data Scientists?

AutoML is exploding in popularity. Here’s how that changes things.

Frederik Bussler
Towards Data Science
5 min readJul 5, 2020

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Photo by Alvaro Reyes on Unsplash

Background

In 2012, an arXiv report on Auto-WEKA was released, describing an automated approach to selecting a machine learning algorithm, features, and hyperparameters, in the hopes that it would “help non-expert users” in the field.

More recently, AutoML has exploded in popularity, with all the big tech players entering the space.

News Coverage of “automl” by CBInsights.

In April 2016, Facebook declared that AutoML was a “backbone” of its AI. In January 2018, Google announced Cloud AutoML. In August 2018, Salesforce open-sourced its Einstein AutoML library. A month later, Microsoft introduced AutoML to its Azure AI platform. Earlier this year, Amazon introduced AutoGluon, another open-source AutoML toolkit.

The State of AutoML

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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