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Any video content platform experience can now be vastly improved with AI-based video recommendation that suggests the most optimal and personalized videos for each user. Community powered AI “Superclusters” The use of AI in enterprise products and services is booming (Forbes on how businesses are using AI in 2023 article)

FedML AI platform is democratizing large language models (LLMs) by enabling enterprises to train their own models on proprietary data. Today, we release FedLLM, an MLOps-supported training pipeline that allows for building domain-specific LLMs on proprietary data. The platform enables data collaboration, computation collaboration, and model collaboration, and supporting training

We are excited to announce that FedML has successfully reached its one-year milestone! Throughout this past year, we have upgraded our product portfolio going beyond federated learning, and enhanced it into a full stack of AI platforms with four interconnected MLOps: Community-Driven AI, FedML Train on the Edge, FedML Train

Note: This article was originally posted at USC Viterbi: https://viterbischool.usc.edu/news/2022/05/new-startup-brings-ai-to-the-people-and-organizations/ SALMAN AVESTIMEHR (LEFT) AND CHAOYANG HE’S NEW STARTUP PROMISES TO MAKE FEDERATED MACHINE LEARNING POSSIBLE ANYWHERE (PHOTO CREDIT: USC VITERBI) For three years, Salman Avestimehr, USC Viterbi Dean’s professor of electrical

Federated learning (FL) is a machine learning paradigm where many clients (e.g., edge servers or mobile/IoT devices) collaboratively train a model while keeping the training data decentralized. It has shown huge potential in mitigating many of the systemic privacy risks, regulatory restrictions, and communication costs resulting from the