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 that less than 20% have brought deep learning projects beyond a pilot stage. It’s well known that most businesses face challenges deploying AI in production, and that’s led to the rise of markets that serve needs like AIOps or auditing startups. In the latest news of advance 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 technology Apache TVM machine learning compiler framework. TVM is currently being used by companies like Amazon, AMD, ARM, Facebook, Intel, Microsoft and Qualcomm. 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 round was led by Addition Capital 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 raised in October 2019, just months after the company was founded. OctoML has remote employees across the United States but is based in Seattle.