Automated machine learning (AutoML)

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Published: Nov 20, 2019
Nov 2019

The power and promise of machine learning has created a demand for expertise that outstrips the supply of data scientists who specialize in this area. In response to this skills gap, we've seen the emergence of Automated machine learning (AutoML) tools that purport to make it easy for nonexperts to automate the end-to-end process of model selection and training. Examples include Google's AutoML, DataRobot and the H2O AutoML interface. Although we've seen promising results from these tools, we'd caution businesses against viewing them as the sum total of their machine-learning journey. As stated on the H2O website, "there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models." Blind trust in automated techniques also increases the risk of introducing ethical bias or making decisions that disadvantage minorities. While businesses may use these tools as a starting point to generate useful, trained models, we encourage them to seek out experienced data scientists to validate and refine the results.