Abstract: A method for integrating a machine learning (ML) model that impacts different factor groups for generating a dynamic recommendation to collectively optimize an objective factor is provided.

Abstract: A system and method for automatically predicting deviation on a metric of a use-case and deriving interconnections between metrics for generating action recommendations is provided. The system includes a deviation management system 104 which captures data from a plurality of external sources and internal sources and comprises of a deviation management platform 106 and a deviation management environment 108. The system includes various computation modules which work the deviation management platform 106 to provide a deviation management service to a set of clients that are associated with that service. The service and its users are specific to use-case, wherein the use-case is specified by a client device 116 inside the system. The system comprises of external data which is horizontal across a plurality of deviation management services and internal data which is specific to every deviation management service.

Abstract: An AI platform to enable one or more users to design and create AI enabled applications is provided. The AI platform comprises a data module configured to condition data received from a plurality of data sources to generate a corresponding data pipeline; wherein the data module comprises a plurality of reusable data components. The AI platform further comprises an intelligent processing module configured to process a plurality of datasets received on the data pipeline and generate a corresponding artificial intelligence (AI) pipeline; wherein the intelligent processing module comprises a plurality of reusuable data processing components.

Abstract: A method for estimating metric forecasts associated with a plurality of related entities with more accuracy by training and applying a metric forecast entity relationship machine learning (ML) model is provided. The method includes obtaining a first primary and a first secondary entity metric forecast based on historical data of a primary entity metric obtained from primary entity metric device and historical data of secondary entity metric obtained from secondary entity metric device at different instances of time, training metric forecast entity relationship ML model based on relationship between first primary and first secondary entity metric forecast to obtain a trained metric entity relationship ML model that accounts for the relationship between the first primary entity metric forecast and the first secondary entity metric forecast, and estimating a second primary entity metric forecast and a second secondary entity metric forecast based on the trained metric entity relationship ML model.

Abstract: A system and method for automatically predicting deviation on a metric of a use-case and deriving interconnections between metrics for generating action recommendations is provided. The system includes a deviation management system 104 which captures data from a plurality of external sources and internal sources and comprises of a deviation management platform 106 and a deviation management environment 108. The system includes various computation modules which work the deviation management platform 106 to provide a deviation management service to a set of clients that are associated with that service. The service and its users are specific to use-case, wherein the use-case is specified by a client device 116 inside the system. The system comprises of external data which is horizontal across a plurality of deviation management services and internal data which is specific to every deviation management service.

Abstract: An AI platform to enable one or more users to design and create AI enabled applications is provided. The AI platform comprises a data module configured to condition data received from a plurality of data sources to generate a corresponding data pipeline; wherein the data module comprises a plurality of reusable data components. The AI platform further comprises an intelligent processing module configured to process a plurality of datasets received on the data pipeline and generate a corresponding artificial intelligence (AI) pipeline; wherein the intelligent processing module comprises a plurality of reusuable data processing components.