5 Reasons Why AutoML is hated by Data Scientists

For the last few weeks, I have been talking about AutoML, where it can be used, and how cool is it. But at the same time, the Data community is still resisting to use of the Auto ML technology due to some major concerns which I feel are totally okay.

In this story, I have compiled some major reasons why data professionals are still reluctant to use the Auto ML technology.

Limitations of AutoML (Image credits: AnalyticsIndiamag)

Data science is highly skilled, part science in understanding how machine learning works, part art in applying data to a domain problem. As such for any nontrivial problem a data scientist needs to work with an automation solution and this is where most of these tools fall down.

Limitations of Auto ML

1. You don't have a lot of control
   Control is invariably the major criticism, having a cookie cutter solution is great as long as you want star shaped cookies, as soon as the data scientist wants to get in and tweak the solution they have to start from scratch outside the platform. Most platforms have tried to counter this by providing lots of advanced User Interface (UI) controls to try and adapt the solution but being UI based it’s pretty clunky and quite far from how they’d usually work.
2. Its a black box process
   Even if any Auto ML platform lets you figure out exactly what it’s…