AutoML: the Promise vs. Reality According to Practitioners

The current conversation about automated machine learning (AutoML) is a blend of hope and frustration. Automation to improve machine learning projects comes from a noble goal. By streamlining development, ML projects can be put in the hands of more people, including those who do not have years of data science training and a data center at their disposal. End-to-end automation, while it may be promised by some providers, is not available yet. There are capabilities in AutoML, particularly in modeling tasks, that practitioners from novice to advanced data scientists are using today to enhance their work.