AutoML Tools Emerge as Data Science Difference Makers Posted on : Aug 29 - 2019

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. “We expect this market to grow substantially as products get better and awareness increases of how these tools fit in the broader data science, ML, and AI landscape,” Forrester analysts Mike Gualtieri and Kjell Carlsson write in a May Forrester New Wave report on the AutoML market.

Gartner, meanwhile, says that by 2020, more than 40% of data science tasks will be automated. That will boost the productivity of citizen data scientists, which as a group is growing 5x faster than professional data scientists, Gartner says.

In the May report, Forrester analysts ranked DataRobot, H2O.ai, and dotData as the three leading providers of AutoML solutions out of a field of about 10. DataRobot received high marks across the board and has the early lead in the field, but H2O.ai is right there with its Driverless AI solution, which Forrester days is mainly geared toward empowering existing data scientists.

In terms of the number of customer deployments, H2O.ai and DataRobot are far and away the biggest vendors. They’ve also been around longer, which has allowed venture capitalists to invest nearly $225 million in DataRobot and $147 million in H2O.ai, about half of which was announced last week.

dotData, which was spun out of NEC in 2018, was characterized by Forrester as a bit of a dark horse candidate. It has a solid set of capabilities – particularly around feature engineering — but not a lot of market recognition as of yet.

“Currently the AutoML market is very hot and growing very rapidly,” says Ryohei Fujimaki, the founder and CEO of dotData. “We are still early in market development compared to the other two….We started in the Japanese market in 2016. In the US market, our market awareness is increasing.”

Fujimaki says a majority of dotData customers are citizen data scientists who use dotData’s GUI tool to lead them through the process of building machine learning models. More advanced users are employing a second product Python-interface that gives them more control. View More