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.

This toolkit is intended to facilitate managing the lifecycle of these special configurations by leveraging vSphere APIs.

This Fling provides a platform for Data Scientists to quickly setup a virtualized cloud infrastructure to conduct data science experiments.

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.

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.

vAssist.ai, a Natural Language Processing (NLP) platform, enables bot developers to train machine learning models for intent classification and entity extraction.