FedML, a Sunnyvale, CA-based custom AI development company, raised $11.5M in Seed funding.
According to press release, this $11.5m seed round includes $4.3M in a first tranche previously disclosed in March 2023, plus $7.2M in a second tranche that closed earlier this month. In March, FinSMEs covered a $6m amount raise in Pre-seed and seed.
The round was led by Camford Capital, along with participation from Road Capital, Finality Capital Partners, PrimeSet, AimTop Ventures, Sparkle Ventures, Robot Ventures, Wisemont Capital, LDV Partners, Modular Capital and University of Southern California (USC).
The company intends to use the funds to expand operations and its business reach.
Led by CEO Salman Avestimehr, and CTO Chaoyang He, FedML specializes in custom AI development, using distributed AI and federated learning to help companies build and train their own AI models. Its enterprise software platform and open-source library empower developers to train, deploy and customize models across edge and cloud nodes at any scale. Its distributed MLOps platform enables sharing of data, models, and compute resources in a way that preserves data privacy and security.
FedML empowers distributed machine learning via both edge and cloud resources, through innovations at three AI infrastructure layers:
- An MLOps platform that simplifies training, serving, and monitoring generative AI models and LLMs in large-scale device clusters including GPUs, smartphones, or edge servers;
- A distributed and federated training/serving library for models in any distributed settings, making foundation model training/serving cheaper and faster, as well as leveraging federated learning to train models across data silos; and
- A decentralized GPU cloud to reduce the training/serving cost and save time on complex infrastructure setup and management via a simple “fedml launch job” command.
FedML recently introduced FedLLM, a customized training pipeline for building domain-specific large language models on proprietary data. FedLLM is compatible with LLM libraries such as HuggingFace and DeepSpeed, and is designed to improve efficiency, security and privacy of custom AI development. To get started, developers need to add some lines of source code to their applications. The FedML platform then manages the complex steps to train, serve and monitor the custom LLM models.
FedML’s platform is now used by more than 3,000 users globally, performing more than 8,500 training jobs across more than 10,000 edge devices. The company has also secured more than 10 enterprise contracts spanning healthcare, retail, financial services, smart home/city, mobility and more.
FinSMEs
20/07/2023