Abstract: Embodiments relate to systems and methods that create a final model by parallel training of models executed within separate containers. A master job present within one container, performs pre-processing (e.g., noise reduction; duplicate removal) of incoming data. The master job orchestrates the training of individual models by child jobs that are executed in parallel within respective separate containers. After checking the status of completion of the child jobs (e.g., via HTTP or by reading local progress files) the master job references the trained models in order to determine a final model. This final model determination may comprise aggregating the trained models, or selecting one model based upon a metric (such as a f1 score). Parallel training of models by child jobs executed within separate containers, streamlines and accelerates model creation. Particular embodiments may be suited to train a model that identifies unique entities from incoming data including names and addresses.

Abstract: An employee of a large organization sends a human-readable document such as an email or text message to another employee of the organization to inform the other employee of a change in availability. A trained machine-learning model extracts, from the human-readable document, data used by a leave management system (LMS) to formalize and memorialize the leave request. For example, the employee name, manager name, date leave begins, date leave ends, reason for the leave request, or any suitable combination thereof may be determined by the machine-learning model based on the human-readable document. The extracted data is provided to the LMS and the leave request is created.