A Data Science Central Community
Summary: There are several things holding back our use of deep learning methods and chief among them is that they are complicated and hard. Now there are three platforms that offer Automated Deep Learning (ADL) so simple that almost anyone can do it.
A small percentage of our data science community has chosen the path of learning these new techniques, but it’s a major departure both in problem type and technique from the predictive and prescriptive modeling that makes up 90% of what we get paid to do.
Artificial intelligence, at least in the true sense of image, video, text, and speech recognition and processing is on everyone’s lips but it’s still hard to find a data scientist qualified to execute your project.
Actually when I list image, video, text, and speech applications I’m selling deep learning a little short. While these are the best known and perhaps most obvious applications, deep neural nets (DNNs) are also proving excellent at forecasting time series data, and also in complex traditional consumer propensity problems.
Last December as I was listing my predictions for 2018, I noted that Gartner had said that during 2018, DNNs would become a standard component in the toolbox of 80% of data scientists. My prediction was that while the first provider to accomplish this level of simplicity would certainly be richly rewarded, no way was it going to be 2018. It seems I was wrong.
Here we are and it’s only April and I’ve recently been introduced to three different platforms that have the goal of making deep learning so easy, anyone (well at least any data scientist) can do it.