Sematic is the only platform to offer end-to-end continuous machine learning automation for the next million data scientists and machine learning engineers

Sematic, an open-source continuous machine learning (ML) platform, today announced it has closed a $3 million seed funding round led by Race Capital with participation from Y Combinator, Soma Capital, Leonis Capital, Pioneer Fund and renowned angel investors including Brandon Leonardo, co-founder of Instacart; Oliver Cameron, VP of Product at Cruise; and Jeremy Stanley, former VP of Data Science at Instacart and co-founder of Anomalo.

Sematic, founded by Emmanuel Turlay, a founding member of Cruise’s ML Infrastructure team, is the first and only platform to offer end-to-end continuous machine learning automation for ML engineers. With Sematic, machine learning engineers can automate, schedule, and clone pipelines whenever new labeled data is available. Companies can use Sematic to scale up their machine learning teams and focus on training new models instead of worrying about maintaining the necessary infrastructure needed for automation.

The platform offers a lightweight, open-source ML and Data Science pipeline development and execution framework with an easy onboarding experience. Machine learning engineers can simply use native Python to develop and run arbitrary end-to-end pipelines that track and version all assets and artifacts (models, datasets, plots, metrics, code, etc.), and visualize them in an intuitive and comprehensive user interface.

According to Gartner, AI-derived business value is forecast to reach $3.9 trillion in 2022. Despite the increase in the usage of artificial intelligence and machine learning within Fortune 500 enterprises, machine learning is often underused, or even misused with no industry standards around continuous deployment, automation and integration. According to VentureBeat, 87% of AI projects will never make it into production.

“At Cruise, in order to deploy more ML models to autonomous cars, we had to grow the Infrastructure teams linearly with the number of ML Engineers and models to support. This costly organizational strain led me to realize that making ML pipelines automated and ML engineers autonomous is not just a nice-to-have, but a requirement in order to ship more, better, and safer models,” said Mr. Turlay, CEO of Sematic. “Once we put the right ML platform in place, the productivity of Cruise’s ML teams exploded, enabling them to reach launch goals.” Turlay and his team were Tech Leads of the ML Infrastructure team at Cruise.

“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 Turlay.

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.

“​​Sematic is exactly the kind of machine learning platform we want at Voxel. It gives my ML team unparalleled visibility into our ML pipelines and just the right level of abstraction for us to focus on business logic and leverage cloud resources without requiring infrastructure skills,” said Anurag Kanungo, CTO and Co-founder of Voxel.

“Data is the new oil and Sematic is building the new oil rig for the ML engineer team,” said Alfred Chuang, Partner at Race Capital. “Today, ML engineers heavily rely on their infrastructure team for testing, automation, and deployment of ML models. This inefficiency leads to more than 80% of all trained ML models never making it to production. We are proud to partner with Emmanuel and his team to empower the next million data scientists and ML engineers and beyond. This is a crucial problem whose solution will push the entire data and machine learning industry forward.”

Sematic will accelerate its hiring process, launch its hosted cloud offering and continue to attract more developers to its best-in-class machine learning platform.