Abstract: Application compliance policies can be dynamically updated. An agent can be deployed on user computing devices to monitor application install events to track how frequently users install applications on the user computing devices and whether applicable application compliance policies dictate that such applications should be removed. A policy management service of a management solution can interface with the agents to gather application metrics and use the application metrics to dynamically update the application compliance policies to prevent future removal of one or more applications that are determined to be commonly installed by the users. These dynamic updates could be made on a per-user basis, a per-group basis, or a global basis.

Abstract: A machine learning model trained with a triplet loss function classifies input strings into one of multiple hierarchical categories. The machine learning model is pre-trained using masking language modeling on a corpus of unlabeled strings. The machine learning module includes an attention-based bi-directional transformer layer. Following initial training, the machine learning model is refined by additional training with a loss function that includes cross-entropy loss and triplet loss. This provides a deep learning solution to classify input strings into one or more hierarchical categories. Embeddings generated from inputs to the machine learning model capture language similarities that can be visualized in a cartesian plane where strings with similar meanings are grouped together.