Dynamic communication routing at contact centers

Abstract

This disclosure describes management of a contact center executing in a service provider network. The management may be performed by using one or more trained deep learning/machine learning (ML) models. The ML models may be trained using metrics and data gathered from the various services and systems in the service provider network used to implement the contact center. The trained models may be used for forecasting staffing needs, e.g., agents, for the contact center, scheduling agents, detecting anomalies with regard to customer traffic, e.g., received communications from customers, at the contact center, adherence of agents to the scheduling, and dynamically routing of received communications to appropriate agents (e.g., based on skills of the agent and other factors) for handling of the communications.