DataRobot Celebrates One Billion Models Built on Its Cloud Platform
Apr 16, 2019 by Libby Botsford
Milestone comes on the heels of significant company growth in 2018
BOSTON, April 16, 2019 —DataRobot, the leader in automated machine learning, today announced that its customers have built one billion models on its Amazon Web Services (AWS) cloud platform — a major milestone in AI adoption. DataRobot customers from around the world are using these machine learning models to better understand and glean actionable insights from accessible data.
Leveraging the scalability of AWS and the processing power of Intel® Xeon® processors, the DataRobot Cloud platform automates the data science workflow, enabling automation-first data scientists and citizen data scientists to build and deploy the most accurate predictive models in minutes. With the intelligence afforded by the DataRobot platform, organizations make informed decisions to improve productivity and efficiency, support business objectives, and increase revenue.
“As a compute-intensive application, our cloud environment provides organizations with a flexible and scalable way to build the machine learning models required to improve business processes and impact business results,” said Phil Gurbacki, VP of Product Management, DataRobot. “Our customers build more than two and a half million models every day, and with each model, our solution gets smarter and more sophisticated. Having now learned from a billion models, DataRobot is putting the power of machine learning into the hands of users across a growing number of use cases, delivering real value to organizations across the globe.”
The DataRobot platform hosts models that serve organizations from a range of industries, including healthcare, banking, manufacturing, retail, and information technology. The models determine, for instance, if a customer is going to churn; if a patient will be readmitted to a hospital; if an insurance candidate is likely to default on a loan; and even when a movie script is poised to become a success. DataRobot enables organizations to build trustworthy AI, providing human-friendly explanations for how the AI is trained, what patterns the AI finds in the data, and even the reasons the AI makes decisions. DataRobot’s industry-leading automation reduces human error via built-in guardrails to ensure best practices are followed, as well as automated model training and deployment.
To meet the growing demand for its automated machine learning solution, DataRobot also made several strategic acquisitions to enhance its capabilities — in February 2019, the company acquired data collaboration platform provider Cursor to bolster its data management abilities, and in July 2018, DataRobot acquired Nexosis to further the company’s quest to democratize data science enabling the AI-driven enterprise. DataRobot also increased its employee count by more than 90 percent, bringing total employees to more than 600 across its offices in North America, Europe, Australia, Asia, and South America.
For more information on DataRobot and its one billion milestone, check out this video.
DataRobot is the category creator and leading provider of automated machine learning. Organizations worldwide use DataRobot to empower the teams they already have in place to rapidly build and deploy machine learning models and create advanced AI applications. With a library of hundreds of the most powerful open source machine learning algorithms, the DataRobot platform encapsulates every best practice and safeguard to accelerate and scale data science capabilities while maximizing transparency, accuracy, and collaboration.
By making data scientists more productive and enabling the democratization of data science, DataRobot helps organizations transform into AI-driven enterprises. With offices around the globe, DataRobot is backed by $225 million in funding from top-tier firms, including New Enterprise Associates, Sapphire Ventures, Meritech, and DFJ. For more information, visit www.datarobot.com, and join the conversation on Twitter and LinkedIn.