Our investment in Sematic: Open Source Continuous Machine Learning Platform
We are excited to announce that Race Capital has led the $3M Seed investment into Sematic, an Open Source Continuous Machine Learning Platform, partnering with Emmanuel Turlay, and the entire Sematic team.
Sematic is the first and only platform to offer end-to-end Continuous Machine Learning automation for ML engineers. With Sematic, ML engineers can automate, test, schedule, and clone pipelines whenever new labeled data is available. Companies can use Sematic to scale up their ML teams and focus on developing and training new models instead of worrying about maintaining the necessary infrastructure needed.
We believe that Machine Learning needs better development frameworks and what Emmanuel is building with Sematic is the solution to empower developers and companies of all sizes.
Technical and Strong founder market fit from Cruise’s ML Infrastructure team
We at Race Capital always look for strong technical founders and founding teams that can understand and resonate with the pain points of the problems they are aiming to tackle, and Emmanuel and the Sematic team is a perfect example of this.
Before starting Sematic, Emmanuel was previously a Tech lead on the Machine Learning Infrastructure team at Cruise, where he helped grow the team to 80 engineers and built crucial internal tooling that drastically improved productivity and democratized data access across Cruise. The rest of the Sematic founding team also previously worked with Emmanuel at Cruise.
Before Cruise, Emmanuel was a Senior Software Engineer at Instacart and was also previously a PhD researcher at CERN. His combined experience in academia and big tech applications makes him uniquely equipped to provide the best practices for an end-to-end pipeline from notebook to production-ready ML models.
Simple and intuitive development framework
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.
Ruby on Rails is an intuitive web development framework to build quick and robust web applications. We believe that Sematic can be the Ruby on Rails for Machine Learning.
Serving the ever growing, yet inefficient, Machine Learning market
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. Gartner predicts that 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization and 87% of AI projects will never make it into production, according to VentureBeat.
For AI/ML to be further proliferated and adopted by large tech companies and AI/ML departments of traditional businesses, an intuitive collaborative platform that facilitates easier development of ML models like Sematic would be needed.
We look forward to continue working closely with Emmanuel and the Sematic team to empower the next millions of data scientists and ML engineers and push the entire Data and Machine Learning industry forward!
Alfred
