3 Reasons Why AutoML Won't Replace Data Scientists Yet - KDnuggets

Automatic Machine Learning (aka AutoML) has been gaining traction within the Data Science community. This surge of interest is reflected on the development and release of numerous open source AutoML libraries (e.g., AutoWeka, MLBox, auto-sklearn, TPOT, HpBandSter, AutoKeras, prophet), and on the emergence of businesses focused on building and commercialising AutoML systems (e.g., DataRobot, DarwinAI, H2O.ai, OneClick.ai). Although AutoML is a hot topic and many articles are being written about it (e.g., H2O.ai's Erin LeDell, Fast.ai's Rachel Thomas, and KDnuggets' Matthew Mayo), just a few have emphasized and clarified the limitations of current AutoML systems. It is our intention to address this gap by pointing out what we believe to be AutoML's main drawbacks currently.