Scaleout offers a platform for secure data sharing and federated learning, enabling organizations to collaborate on machine learning projects while ensuring data privacy and compliance. This approach allows companies to enhance model training by leveraging combined datasets without compromising sensitive information.
Funding
$2.4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional machine learning approaches require centralizing data, which can be infeasible due to data privacy regulations, security concerns, or the distributed nature of edge devices. This limitation hinders the ability of organizations to collaborate on machine learning projects and leverage combined datasets for enhanced model training.
Solution
Scaleout offers a federated learning platform, FEDn, that enables secure, scalable AI across the edge-to-cloud continuum. Unlike conventional machine learning, Scaleout orchestrates model training and deployment from the device edge to the cloud, preserving data privacy while enabling continuous intelligence. The FEDn framework allows projects to migrate from simulated environments to full deployment without code modifications. This approach allows organizations to train models on distributed data without centralization, enabling secure, scalable machine learning across edge devices in connected environments.
Target Audience
Scaleout's primary customers are organizations in industries with edge computing challenges, such as automotive, defense, industrial IoT, and automation.
Features
- Federated learning development from local testing to full deployment
- Orchestrates model training and deployment from device edge to cloud
- Supports secure, scalable machine learning across edge devices in connected environments
- Enables fleet learning across distributed vehicles for improved perception, predictive maintenance, and autonomous capabilities
- Allows training models across organizational boundaries without compromising sensitive data
- Facilitates real-time inference at the edge, improving efficiency and quality control by leveraging machine learning across distributed sensors and equipment