![]() "Whether it's a 300- or 3,000-person team, it's remarkable how manual most of this work has remained, and how understaffed these teams can be as a result. "A lot of people aren't aware of just how expensive and time intensive it is for businesses to collect the information they need, and how prohibitive that can be for the data they'd like to be capturing," said HyperScience CEO Peter Brodsky. The new round of financing will allow HyperScience to further invest in engineering and product development, sales and marketing, and a rapid expansion of the team across all functions. Existing investors FirstMark Capital and Felicis Ventures as well as new investors Battery Ventures, Global Founders Fund, TD Ameritrade and QBE also participated in the round, bringing the company's total funding to $50 million. We were, ‘oh wow, so we can get even faster here and capture this business.’ We have a business opportunity right in front of us, but we need to scale fast to take full advantage of it,” Ceze said.HyperScience, a leading edge machine learning company offering enterprise-grade solutions for automating office work at scale, today announced a $30 million Series B funding round led by Stripes Group, a New York-based growth equity firm with investments in a number of advanced software, data analytics and enterprise technology companies, including Flatiron Health, Sift Science, SPINS and Upwork, among others. “We looked at all of our opportunities in hardware enablement, in accelerating the SaaS business and cloud enablement. “If you make something twice as fast on the same hardware, making use of half the energy, that has an impact at scale.” Increasingly, he noted, the large cloud providers also hit capacity limits for deploying high-end GPUs (there is a chip shortage, after all), so being able to move their models to a different GPU or maybe even to a CPU is another advantage.Ĭeze noted that the company didn’t have to raise new funding at this point, but that the team decided to be opportunistic despite still having a healthy runway. “It’s not only a cost issue but also a sustainability issue,” he noted. ![]() As Ceze noted ahead of today’s announcement, that service hasn’t quite reached general availability yet, but OctoML is now working with a larger number of customers and is focused on making them successful on its platform.Īs models proliferate and get more sophisticated, deploying them in the cloud is also getting more expensive, Ceze noted, so a system that can optimize these models immediately leads to cost savings for the company’s customers. The company says its users, which feature a number of Global 100 companies, including Toyota, are seeing a 2-10x improvement in their ML model performance after using its service.Īround the time of its Series B earlier this year, the company had just started onboarding some early adopters to its SaaS platform. ![]() The company also recently worked with Microsoft on a project about deploying video content moderation at scale. OctoML builds on TVM’s ability to automatically optimize machine learning models and allow them to run on virtually any hardware.Īs Ceze told me, since raising its Series A round, the company has signed up a number of hardware partners, including Qualcomm, AMD and Arm. TVM is currently in use by the likes of Amazon, Microsoft and Facebook. The company was co-founded by CEO Luis Ceze, CTO Tianqi Chen, CPO Jason Knight, Chief Architect Jared Roesch and VP of Technology Partnerships Thierry Moreau, who together also created the Apache TVM open source machine learning compiler framework. Previous investors Addition, Madrona Venture Group and Amplify Partners also participated in this round, which brings the company’s total funding to $132 million, including a $28 million Series B round it announced earlier this year. OctoML, a Seattle-based startup that helps enterprises optimize and deploy their machine learning models, today announced that it has raised an $85 million Series C round led by Tiger Global Management.
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