FedML, a Sunnyvale, CA-based startup that aims to help companies train, deploy, monitor, and improve AI models on the cloud or edge, raised $11.5 million in seed funding at a valuation of $56.5 million.
The round was led by Camford Capital, with participation from Road Capital and Finality Capital.
FedML provides a collaborative AI platform that enables companies and developers to work together on AI tasks by sharing data, models, and compute resources. Customers can run custom AI models or models from the open-source community on the platform.
The company recently introduced FedLLM, a training pipeline for building domain-specific large language models on proprietary data, compatible with popular LLM libraries such as Hugging Face and Microsoft's DeepSpeed.
The platform has attracted around 10 paying customers, including a "tier one" automotive supplier, and claims to have over 3,000 users globally, performing over 8,500 training jobs across more than 10,000 devices.
“FedML enables custom AI models by empowering developers and enterprises to build large-scale, proprietary, and private LLMs at less cost,” Salman Avestimehr, Co-founder and CEO of FedML said. “What sets FedML apart is the ability to train, deploy, monitor, and improve ML models anywhere and collaborate on the combined data, models, and compute — significantly reducing the cost and time to market.”
Company: FedML, Inc.
Round: Seed Round
Funding Month: July 2023
Lead Investors: Camford Capital
Additional Investors: https://fedml.ai/
Company Website: Road Capital and Finality Capital
Software Category: MLOps platform and Collaborative AI
About the Company: Founded by Salman Avestimehr and Chaoyang He, FedML provides an open-source community and an enterprise platform for decentralized and collaborative AI, and a web 3 AI marketplace for everyone to monetize their data, ML models, and AI applications. FedML’s enterprise software platform and open-source library empower developers to train, deploy and customize models across edge and cloud nodes at any scale. FedML’s distributed MLOps platform uniquely enables the sharing of data, models, and compute resources in a way that preserves data privacy and security.