AutoML Tools Emerge as Data Science Difference Makers

The days of handcrafted algorithms aren't quite over, but it's hard to dismiss to impact that automated machine learning (AutoML) is having on the data science field. As companies look to imbue intelligence into their products and services, AutoML tools will lower the barrier of entry into data science and open the door for data-driven automation on vast scales. In the past few years, we've seen a surge of interest in AutoML tools, which automate a range of tasks in the data science workflow. While automated ML features may be found in a range of tools, the AutoML category has a fairly defined set of features, including: acquiring and prepping data; engineering features from the data; selecting the best algorithm; tuning the algorithm; and deployment and monitoring of production models. Forrester says just about every company will have a stand-alone AutoML tool.