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AI Unicorn DataRobot Simplifies Data Modeling

Posted on Thursday, Oct 17th 2019

According to a report by Research On Global Markets, the global machine learning market is expected to grow 48% annually to $19.4 billion by 2023. The growing adoption of IoT devices and of a connected business ecosystem is expected to drive data-powered decisions, thus fueling the requirement for machine learning technologies.

DataRobot’s Offerings

Boston-based DataRobot was set up in 2012 by data scientists Jeremy Achin and Thomas DeGodoy. The two founders quit their jobs at property casualty insurer Travelers to set up the company. Jeremy was tired of the bureaucracy that is seen in large organizations and wanted to start a company of his own that would automate a lot of work that data scientists did. He connected with Thomas, who was a senior director of research and modeling for the firm, to build DataRobot.

DataRobot enables enterprises to leverage the power of AI by helping them turn data into meaningful information for predictive modeling. Its platform empowers the teams already in place to build and deploy machine learning models, create advanced AI applications, and more effectively manage, monitor and govern AI across any infrastructure and environment. It uses parallel processing capabilities to train and evaluate thousands of models in R, Python, Spark MLlib, H2O, and other open-source libraries. It searches through millions of possible combinations of algorithms, pre-processing steps, features, transformations, and tuning parameters to deliver the best models for dataset and prediction target.

Once a user enters its data set and the requirements, DataRobot’s software runs a competition within itself by testing out several solutions to the problem. It then provides an analytical model that is expected to provide the most accurate predictions. It has built a model that has a very simple and easy-to-use front end to make it easier for business analysts and executives to use its solution. DataRobot admits that it cannot do everything that a data scientist can, but it can solve many data modeling problems.

Its use cases span across various industries including banking, sports, casinos, retail, and healthcare, to name a few. For example, within the banking sector, its platform can be used to automatically assess credit default risk and track fraudulent transactions. Within the healthcare segment, its platform helps predict hospital readmission risk and model hospital capacity. Its customers include names like Humana, United Airlines, Harvard Business School, and Deloitte.

DataRobot’s Financials

DataRobot is privately held and does not disclose its financials. It claims it has had triple-digit recurring revenue growth since 2015. It has raised $431 million from investors including Sands Capital Ventures, Geodesic Capital, Intel Capital, Sapphire Ventures, AllianceBernstein, Meritech Capital Partners, Tiger Global Management, EDBI, DFJ Growth, and World Innovation Lab (WiL). Its last round of funding was closed earlier last month when it raised $206 million at a valuation of $1 billion. The round was led by Sapphire Ventures. It plans to use these funds to continue to build its product portfolio.

DataRobot has plenty of competition in the space. Smaller competitors include names like Domino Data Lab, Dataiku, and Algorithmia. But there are several bigger companies like Amazon, Oracle and SAP that are also offering advanced AI capabilities. Last year Oracle entered the market by acquiring DataScience for an undisclosed sum. DataScience was a much smaller player which had raised $28 million in funding and was operating at annual revenues of $7.8 million.

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