Abstract: Aspects of the disclosure relate to using machine learning models to automatically deploy computing workloads. A computing system may retrieve resource data. The resource data may comprise deployment costs of computing workloads that are currently deployed, indications of computing workloads that are preauthorized for automatic deployment, and indications of computing workloads that are not preauthorized for automatic deployment. Cloud service provider data indicating cloud service provider costs may be retrieved, via an application programming interface (API) connector. Based on inputting the resource data and the cloud service provider data into machine learning models, cloud deployment data may be generated. The cloud deployment data may comprise predicted deployment costs of the cloud service providers. The computing workloads that are preauthorized for automatic deployment may be deployed.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for determining if a given network component falls under the scope of dynamic host configuration protocol (DHCP) and automatically provisioning the network component accordingly. The invention is directed to a specific device, communication process, and backend processing approach which provides a tool for analyzing network component qualities and automating server provisioning.