Abstract: A computer-implemented execution platform for executing a machine-learning model programmed and trained on a development platform utilizing a first programming language, a corresponding method and a corresponding computer program product are provided. The execution platform is implemented based on a second programming language and comprises a service container and a model server container. The service container is arranged to receive interrogation requests to interrogate the machine-learning model and to return interrogation responses of the machine-learning model. The model server container hosts an encapsulated instance of the machine-learning model adapted to run on the execution platform utilizing data structures and operations of the first programming language.

Abstract: Systems and methods for implementing a reinforcement machine learning framework for dynamic demand forecasting. A method includes generating estimated booking data for an initial time with a demand model trained using a training set of historical booking data. A variance is detected between the estimated booking data and transient booking data observed at the initial time that exceeds a defined threshold. In response to detecting the variance, a reinforcement learning service is activated. An updated training set including enhanced booking data observed at a subsequent time is created after activating the reinforcement learning service. A parameter of the demand model is updated by training the demand model using the updated training set.

Abstract: Systems and methods for implementing a machine learning framework for demand shock detection for dynamic demand forecasting. A method includes generating predicted booking observations with a demand model trained using a training set of historical booking data. Transient booking observations are obtained from an active database. An observed likelihood score is computed from the transient booking observations based on the demand model trained on the historical booking data. A demand shock threshold is computed based on the statistical relationship between a time to detection of the demand shock event and at least one shock detection criterion. An occurrence of a demand shock event is determined by comparing the observed likelihood score to the demand shock threshold.