Machine learning startup GraphLab raises $18.5M, becomes Dato

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Credit: GraphLab

GraphLab, a Seattle-based startup trying to make machine learning more accessible, has raised an $18.5 million series B round of venture capital and has changed its name to Dato. The company has now raised $25.3 million, with the latest round coming from existing investors NEA and Madrona Ventures and new investors Vulcan Capital and Opus Capital Ventures.

When the company first launched in 2013, it was attempting to commercialize an open source graph-computing project called GraphLab. However, Co-founder and CEO Carlos Guestrin told me, many of its customers were also interested in analyzing different types of data using different techniques — sometimes much more than they were interested in the graph part — and i became obvious a change was needed.

The release of the company’s Create service in July, which includes tools for things such as regression and deep learning models as well as graph processing, was the first step in the process. The decision to change the company name from GraphLab to Dato came about in the last six weeks, Guestrin said.

“Our name no longer matched who we were … and it wasn’t an aspirational name that could grow with us,” he added.

Carlos Guestrin. Source: Carnegie Mellon University

Carlos Guestrin. Source: Carnegie Mellon University

Early Dato customers include Adobe, Zillow, PayPal and Cisco, and many use it recommendation engines and other data mining projects. Dato Create handles everything from building the initial models to rolling them out into production applications, and is aimed at what Guestrin calls “savvy engineers” — folks who understand how to build applications and connect them to a database, perhaps, but who haven’t necessarily taken a machine learning course.

He said Dato hopes to expand its user base even further in time by making it so Create can choose and tune the right algorithms and models.

“The question for me is not keeping up with the Joneses … but trying to understand folks who don’t care about buzzwords,” he said. “What are the capabilities they need?”

When I asked Guestrin if graphs had seen their best days amid a flurry of activity a couple years ago, he said it was just a matter perception. The people most interested in graphs tend to be early adopters of new technologies, he suggested, and as more people got interested in machine learning, the voices talking about graphs got drowned out by people trying to solve different types of problems.

“How many people do you know who wake up in the morning and say, ‘Do you know what’s missing in my life? A graph database,'” he joked.

To learn more about the future of machine learning and some of today’s cutting-edge use cases and technologies, attend Gigaom’s Structure Data conference March 18-19 in New York.

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