Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving training data for multiple datasets that include information about a computing process. The training data is received at a computing system that includes a data manager, a data classifier, and a machine learning (ML) system. The data classifier annotates the training data as being associated with a particular dataset and as being descriptive of computing processes executed to perform transactions. The ML system receives the annotated training data and data about a transaction operation of the system, trains a predictive model to generate prediction data that indicates a runtime condition of the system, and provides the prediction data to a process automation module of the system. The module executes process automation scripts to remediate the computing process, where the computing process is executed by the system to perform the real-time transaction operation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving training data for multiple datasets that include information about a computing process. The training data is received at a computing system that includes a data manager, a data classifier, and a machine learning (ML) system. The data classifier annotates the training data as being associated with a particular dataset and as being descriptive of computing processes executed to perform transactions. The ML system receives the annotated training data and data about a transaction operation of the system, trains a predictive model to generate prediction data that indicates a runtime condition of the system, and provides the prediction data to a process automation module of the system. The module executes process automation scripts to remediate the computing process, where the computing process is executed by the system to perform the real-time transaction operation.