Automated Machine Learning: Myth Versus Realty

Witnessing the data science field’s meteoric rise in demand across pretty much all industries and areas of scientific research, it’s easy to anticipate efforts to create shortcuts to satisfy the need for more data science practitioners. The current trend of automated machine learning is a great case in point. This article will touch on a number of efforts to circumvent the need for data scientists to select and train machine learning models and determine metrics for measuring their performance.

The search for automated approaches in computer science is not new. I can remember as far back as the 1980s when the birth of the Personal Computer triggered a steep advance in the demand for programmers to develop software for the small machines. There were many attempts at “automated programming” and “code generators” designed to advance the idea of point-and-click software development. It never really took off because the goals weren’t realistic, replacing human coders. Something similar is happening today with “automated machine learning”

Should data scientists be concerned? A recent Pew Research Center study provided the percentage of U.S. adults who think certain professions will be replaced by robots or computers in their lifetimes showed that 53% of software developers believed that their jobs would be replaced “somewhat or very likely.”…