OctoML raises $28M for machine learning deployment optimization

OctoML helps businesses accelerate machine learning deployments using Apache TVM and today closed a $28 million Series B funding round …

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It’s well known that most businesses face challenges deploying AI in production, and that’s led to the rise of markets serve those needs. In the latest news of advance for such a company, OctoML today raised a $28 million Series B funding round.

OctoML helps businesses accelerate and deploy AI and relies on the open-source technology Apache TVM machine learning compiler framework. The funding will be used for OctoML to continue building out products like the Octomizer platform and invest in the company’s go-to-market strategy and customer service teams.

“We started the TVM work as a research project at the University of Washington about five years ago, and all the key people in the project are part of, they all got their PhDs and are part of the company now,” OctoML CEO and cofounder Luis Ceze told VentureBeat. “We’re focused on making inference fast on any hardware, and support cloud and edge deployments.” 

Last month, OctoML joined more than 20 startups who have banded together to create the AI Infrastructure Alliance, an effort involving startups like Algorithmia and Determined AI for interoperability between the offerings from AI startups and advance alternatives to popular cloud AI services.

The $28 million funding was led by Addition Capital led the round with participation from existing investors Madrona Venture Group and Amplify Partners.

OctoML has raised $47 million to date. A $3.9 million seed funding round was held in October 2019.


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