Abstract: A method includes a deployment computer receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer. For each model of a plurality of models stored in a database, the deployment computer can determine if a model in the plurality of models is a suitable model based on a likelihood value. The deployment computer can then generate one or more client response messages comprising one or more assets created by one or more suitable models. The deployment computer can then transmit the one or more client response messages comprising the one or more assets to the client computer.

Abstract: Techniques are provided for centrally analyzing data in a cloud environment. Data can be received from a cloud provider and a machine learning model of a unified smart connector is applied to the data in order to create modeled data. An operation status can be determined based on the modeled data and the unified smart connector can independently determine, for example, if additional data is needed, the type of data needed, and the source of the data.

Abstract: A method includes a deployment computer receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer. For each model of a plurality of models stored in a database, the deployment computer can determine if a model in the plurality of models is a suitable model based on a likelihood value. The deployment computer can then generate one or more client response messages comprising one or more assets created by one or more suitable models. The deployment computer can then transmit the one or more client response messages comprising the one or more assets to the client computer.

Abstract: A method includes a deployment computer receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer. For each model of a plurality of models stored in a database, the deployment computer can determine if a model in the plurality of models is a suitable model based on a likelihood value. The deployment computer can then generate one or more client response messages comprising one or more assets created by one or more suitable models. The deployment computer can then transmit the one or more client response messages comprising the one or more assets to the client computer.

Abstract: A method includes a deployment computer receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer. For each model of a plurality of models stored in a database, the deployment computer can determine if a model in the plurality of models is a suitable model based on a likelihood value. The deployment computer can then generate one or more client response messages comprising one or more assets created by one or more suitable models. The deployment computer can then transmit the one or more client response messages comprising the one or more assets to the client computer.

Abstract: Techniques are provided for centrally analyzing data in a cloud environment. Data can be received from a cloud provider and a machine learning model of a unified smart connector is applied to the data in order to create modeled data. An operation status can be determined based on the modeled data and the unified smart connector can independently determine, for example, if additional data is needed, the type of data needed, and the source of the data.