Resource-Efficient Supervised Anomaly Detection Classifier
Resource-Efficient Supervised Anomaly Detection Classifier is a scikit-learn classifier for resource-efficient anomaly detection that augments either Random-Forest or XGBoost classifiers to perform well in a resource-constrained setting.
Supernova - Accelerating Machine Learning Inference
Project Supernova is to build a common machine learning inference service framework by enabling machine learning inference accelerators across edge endpoint devices, edge systems and cloud, with or without hardware accelerators.
Federated Machine Learning on Kubernetes
FATE is an opensource project hosted by Linux Foundation to provide a federated learning framework. FATE has been used to increase the performance of predictions in credit reporting, insurance and other financial areas, as well as surveillance and visual detection projects. It helps organizations to comply with strict privacy regulations and laws such as GRDP and CCPA.