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Managed enterprise AI startup DataRobot today announced an additional $50 million raise from strategic investors Snowflake Ventures, Salesforce Ventures, and Hewlett Packard Enterprise (HPE). Together, the fresh capital expands DataRobot’s series F round led by Altimeter to $320 million at a $2.8 billion valuation. That’s up from $270 million at a $2.7 billion valuation as of November.

The benefits of AI and machine learning can feel intangible at times, but surveys show this hasn’t deterred enterprises from adopting the technology in droves. Business use of AI grew a whopping 270% over the past four years, according to Gartner, while Deloitte says 62% of respondents to its corporate October 2018 report deployed some form of AI, up from 53% a year ago. But adoption doesn’t always meet with success, as the roughly 25% of companies that have seen half their AI projects fail will tell you.

DataRobot says as a part of the funding announced today, it will deeply integrate its products with Snowflake and pursue “joint go-to-market activities.” Additionally, DataRobot plans to strengthen its relationships with Salesforce and HPE, working particularly closely with HPE to deliver multicloud experiences to end users.

Boston-based DataRobot was founded in 2012 and claims to have had triple-digit recurring revenue growth dating back to 2015, as well as 2 billion models built on the platform to date. DataRobot CEO Jeremy Achin was previously director of research and modeling at property casualty insurer Travelers. DataRobot cofounder and CTO Tom de Godoy was a senior director of research and modeling at the same carrier.


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DataRobot’s suite is a portable architecture that runs on cloud platforms, on-premise datacenters, or as a fully managed service. It lets customers prepare data and create and validate machine learning models, including classification, advanced regression, time series, and deep learning algorithms. Once it is deployed, customers can monitor models from a single dashboard and test, run, and maintain them to optimize outcomes that inform decision-making.

Depending on a customer’s needs, DataRobot automatically runs an internal competition by testing hundreds or even thousands of solutions to a problem and delivering the models expected to provide the most accurate predictions. And the platform’s automatic feature engineering, apps, and machine learning model selection features are domain-agnostic.

DataRobot’s customers span more than a third of the Fortune 50, including Kroger, Nationwide, Lenovo, PNC, and others across banking, health care, insurance, finance, manufacturing, retail, government, sports, and gaming verticals.

Using DataRobot, data scientists can explore, combine, and shape datasets into assets ready for AI models courtesy of self-service tools. The platform supports a range of data types and content from traditional tabular data in rows and columns to free-form text, images, and geospatial data.

The company recently launched AI Catalog, which leverages tech from Cursor, a startup DataRobot acquired in February 2019. AI Catalog is designed to help users find data in large organizations and understand how to make it searchable and sharable to bolster collaboration. It complements MLOps, a DataRobot service introduced late last year. MLOps takes existing solutions for modeling and combines them with tools from AI operations company ParallelM, which DataRobot acquired in June 2019. MLOps operates atop Apache Spark and Kubernetes and comes with tools to help organizations deploy models in production, including a dashboard for automatically identifying systems that need to be retrained to improve performance.

The new round brings DataRobot’s total raised to over $750 million, following a $206 million series E in September 2019. This makes the company one of the top-funded AI startups in the world.

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