The days of handcrafted algorithms aren’t quite over, but it’s hard to dismiss to impact that automated machine learning (AutoML) has on the info 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 variety of tasks within the data science workflow. While automated ML features could also be found during a range of tools, the AutoML category features a fairly defined set of features, including: acquiring and prepping data; engineering features from the data; selecting the simplest algorithm; tuning the algorithm; and deployment and monitoring of production models.

Forrester says almost every company will have a stand-alone AutoML tool. “We expect this market to grow substantially as products recover and awareness increases of how these tools slot in the broader data science, ML, and AI landscape,” Forrester analysts Mike Gualtieri and Kjell Carlsson write during a May Forrester New Wave report on the AutoML market.

Gartner, meanwhile, says that by 2020, quite 40% of knowledge science tasks are going to be automated. which will boost the productivity of citizen data scientists, which as a gaggle is growing 5x faster than professional data scientists, Gartner says.


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

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

dotData, which was spun out of NEC in 2018, was characterized by Forrester as a touch of a dark horse candidate. it's a solid set of capabilities – particularly around feature engineering — but not tons of market recognition as of yet.

“Currently the AutoML market is extremely hot and growing very rapidly,” says Ryohei Fujimaki, the founder and CEO of dotData. “We are still early in market development compared to the opposite two….We started within the Japanese market in 2016. within 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 steer them through the method of building machine learning models. More advanced users are employing a second product Python-interface that provides them more control.


dotData seeks to automate these data science functions (image courtesy dotData)

DataRobot is targeting the citizen data scientist, says DataRobot’s Chandler McCann. “Somebody who’s conversant in Excel, somebody who has some quite an affinity towards the info , but not necessarily a knowledge scientist,” he says. “The key requirement to using DataRobot is basically domain knowledge. We lookout of the pc science and therefore the stats part.”

McCann says organizations are turning to AutoML tools due to the problem of machine learning. “You hear about failed projects or not getting the return on investment that they wanted,” he tells Datanami. “The takeaway is that machine learning projects are often hard. That’s why at DataRobot we built a whole success organization to assist organizations clarify what they need to try to to .”

Other AutoML vendors that made Forrester’s cut include Aible, Bell Integrator, Big Squid, DMway Analytics, EdgeVerve, and Squark.

Aible was listed as Strong Performers within the Forrester Wave because of above-average capabilites in user experience, model evaluation and training, and vision. EdgeVerse was also listed as a robust Performer because of above average capabilites in data, model training, and roadmap.

Another new entry into the AutoML business is Databricks. The Apache Spark backer launched its AutoML solution last week with a group of automated features around feature engineering, model search, hyperparameter tuning, and deployment.

Databricks is hoping to empower three groups — data scientists, data engineers, and citizen data scientists – to assist them build machine learning applications. “The common theme we hear from our customers is how can we increase the productivity of of these people?” says Clemens Mewald, Databricks director of product management for machine learning and data science. “As a part of that we are making things easier by bringing in automation.”

There are many AutoML solutions available on the cloud also , with Amazon Web Services Sagemaker getting the foremost attention. With the expansion of the cloud, Google Cloud’s AutoML and Microsoft Azure’s Machine Learning Service also are expected to ascertain many use within the months to return .