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.

Abstract: Technology is described for providing assessments of resource deployments in a computing service environment. A group of related resources may be identified in a computing service environment. The group of related resources may be associated with a single customer. A rating for the group of related resources may be determined based in part on design characteristics of the group of related resources and runtime behavior of the group of related resources. A resource configuration recommendation may be provided based on the rating. The resource configuration recommendation may include alternative resource configurations to improve the rating for the group of related resources.