Sematic, a San Francisco, CA-based open-source continuous machine learning platform, announced that it raised $3 million in seed funding.
The round was led by Race Capital and accompanied by Y Combinator, Soma Capital, Leonis Capital, Fundament, and Pioneer Fund.
The funds will be used to accelerate its hiring process, launch its hosted cloud offering, and continue to attract more developers to its best-in-class machine learning platform.
The platform offers a lightweight, open-source ML and data science pipeline development and execution framework with an easy onboarding experience.
“I want to democratize access to continuous machine learning. Not all businesses can afford to hire dozens of ML infrastructure engineers like we did at Cruise. My team and I are building Sematic as the go-to open-source ML platform for companies of all sizes. Safety and accuracy of machine learning models and empowering ML teams to move much faster is our mission,” said Emmanuel Turlay, CEO of Sematic.
Since its launch in August 2022, Sematic has already closed commercial customers such as Voxel, an AI-powered workplace safety platform, and has gained significant adoption from the open-source community.
Round: Seed Round
Funding Month: November 2022
Lead Investors: Race Capital
Additional Investors: Y Combinator, Soma Capital, Leonis Capital, Fundament, and Pioneer Fund
Company Website: https://www.sematic.dev/
Software Category: open-source Continuous Machine Learning Platform
About the Company: Sematic was founded by Emmanuel Turlay, a founding member of Cruise’s ML Infrastructure team. Sematic is an open-source continuous machine learning platform that enables ML teams to build better and safer ML models faster. Sematic lets ML engineers develop end-to-end pipelines to implement their regression testing and performance improvement strategies. With Sematic, machine learning engineers can automate, schedule, and clone pipelines whenever new labeled data is available. Its mission is to provide machine learning teams across the industry with the easiest way to prototype, automate, and productionize end-to-end ML pipelines while getting unprecedented visibility and observability out of the box.