Abstract: In an approach to deriving highly accurate models, one or more computer processors train a set of machine learning models utilizing a training set and a deep learning algorithm; generate one or more feedback data sets for each model in the set of trained models; rank each model in the set of trained models based on the generated feedback data sets; dynamically adjust one or more thresholds, that initiate a retraining or deployment of one or more ranked models, based, at least in part, on one or more production environment requirements; responsive to exceeding one or more adjusted thresholds, automatically deploy one or more ranked models to one or more deployment environments based, at least in part, on the ranking of the one or more trained models; responsive to not exceeding one or more adjusted thresholds, retrain each model in the set of trained models.

Abstract: Enabling drag and drop operations between disparate file formats is provided. An indication that a user dragged and dropped a source information item having a particular file format into a local application of a data processing system is received by an operating system of the data processing system. The local application utilizes a different file format from the particular file format of the source information item. Relevant content that corresponds to the local application is identified by the operating system in the source information item. The relevant content corresponding to the local application is extracted by the operating system from the source information item. The relevant content extracted from the source information item having the particular file format is incorporated by the operating system into the local application that utilizes the different file format from the particular file format of the source information item for use by the local application.

Abstract: In an approach to deriving highly accurate models, one or more computer processors train a set of machine learning models utilizing a training set and a deep learning algorithm; generate one or more feedback data sets for each model in the set of trained models; rank each model in the set of trained models based on the generated feedback data sets; dynamically adjust one or more thresholds, that initiate a retraining or deployment of one or more ranked models, based, at least in part, on one or more production environment requirements; responsive to exceeding one or more adjusted thresholds, automatically deploy one or more ranked models to one or more deployment environments based, at least in part, on the ranking of the one or more trained models; responsive to not exceeding one or more adjusted thresholds, retrain each model in the set of trained models.