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At its Google Cloud Next conference in San Francisco today, Google announced the launch of a machine learning startup competition, the winner of which will receive cloud credits and $1 million from well-regarded Silicon Valley venture capital firms.
The program is open to startups in the U.S. that have raised less than $5 million and use machine learning software, in any industry vertical. Entrants’ use of the Google Cloud Platform public cloud infrastructure is “encouraged but not required,” according to the application form.
The winning startup gets $1 million in Google Cloud Platform credits, which can cover the cost of computing and storage services as well as fully managed machine learning tools. Employees will get G Suite subscriptions, which includes Gmail, Google Drive, Google Calendar, and other applications — and Google engineers will help startup employees improve their machine learning models.
Additionally, Data Collective and Emergence Capital will offer to invest $500,000 in the winning startup, said Alison Wagonfeld, chief marketing officer for the Google Cloud. Andreessen Horowitz, Greylock Partners, GV (formerly Google Ventures), Kleiner Perkins Caufield & Byers, and Sequoia Capital are also “providing support in connection with this competition,” Wagonfeld said.
Google will give cloud credits to runners-up and finalists as well.
The program might well lead some early-stage startups to consider building on Google Cloud instead of, or in addition to, Amazon Web Services (AWS), the leading public cloud. This contest comes almost three years after Google started offering some startups $100,000 in cloud credits. Amazon Web Services and Microsoft Azure also try to lure qualifying startups with cloud credits, typically in association with accelerator programs like Y Combinator. Emergence Capital in 2015 joined up with Bessemer Capital to invest in startups building on top of Box’s software.
Applications are due by April 16; finalists will be chosen in June, and a final pitch-off will be held in the summer.
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