The ability to use quantum technology to achieve useful tasks, be they scientific or industry related, boils down to precise quantum control. In general it is difficult to assess a proposed solution due to the difficulties in characterizing the quantum system or device. These arise because of the impossibility to characterize certain components in situ, and are exacerbated by noise induced by the environment and active controls. Here, we present a general purpose characterization and control solution making use of a deep learning framework composed of quantum features. We provide the framework, sample datasets, trained models, and their performance metrics. In addition, we demonstrate how the trained model can be used to extract conventional indicators, such as noise power spectra.