Abstract: A system can monitor event data corresponding to a current user experience of a requesting user during a current application session with a network service. Based on the event data, the system generates one or more representations corresponding to the current user experience of the requesting user, and executes a machine learning model to process the one or more representations in order to predict a negative user experience for the requesting user within a future time frame during the current application session. In response to predicting the negative user experience, the system implements one or more corrective actions during the current application session through the service application to prevent or mitigate the predicted negative user experience.

Abstract: A computing system can receive event data corresponding to a user's experience with a network service. Based on the event data, the system can generate a set of representations that correspond to the user's experience with the network service. The representations may be analyzed and/or filtered by an artificial intelligence model executing on the computing system, which can predict negative experiences of users at future time intervals. Based on these predictions, the computing system can implement a set of corrective actions to steer the user experience to a more positive path.

Abstract: A computing system can receive event data corresponding to a user's experience with a network service. Based on the event data, the system can generate a set of representations that correspond to the user's experience with the network service. The representations may be analyzed and/or filtered by an artificial intelligence model executing on the computing system, which can predict negative experiences of users at future time intervals. Based on these predictions, the computing system can implement a set of corrective actions to steer the user experience to a more positive path.