OctoML raises $28M for machine learning deployment optimization


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Studies like the 2020 State of AI report from McKinsey have found that businesses capable of deploying multiple AI models are considered high performers, but a survey of business leaders included in the report found fewer than 20% have taken deep learning projects beyond the pilot stage. It’s well known that most businesses face challenges deploying AI in production, which has led to a rise in startups that serve needs like AIOps or auditing. In the latest news for such a company, OctoML today raised a $28 million series B funding round.

OctoML helps businesses accelerate AI model inference and training and relies on the open source Apache TVM machine learning compiler framework. TVM is currently being used by companies like Amazon, AMD, Arm, Facebook, Intel, Microsoft, and Qualcomm. OctoML will use the funding to continue building out products like its Octomizer platform and investing in its 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 they all got their Ph.D.s 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 — including Algorithmia and Determined AI — that have banded together to create the AI Infrastructure Alliance, an effort to promote interoperability between the offerings from AI startups and advance alternatives to popular cloud AI services.

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

OctoML has raised $47 million to date, including a $3.9 million seed funding round in October 2019, just months after the company was founded. OctoML is based in Seattle with remote employees across the United States.

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