Abstract: There are provided systems and methods for a shared prediction engine for machine learning model deployment. A service provider may provide AI hosting platforms that allow for clients, customers, and other end users to upload AI models for execution, such as machine learning models. A user may utilize one or more user interfaces provided to a client device by the service provider to select machine learning models to perform predictive services based on input features provided in an input string. Thereafter, a machine learning engine may host and execute the models during an instance of the engine provided to the client device. The engine may then process the input features in a processing thread remotely from the client device during the instance so that machine learning predictions may be determined. Thereafter, an output string for the predictions and model explanations may be provided to the client device.

Abstract: Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features.

Abstract: Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features.

Abstract: There are provided systems and methods for a shared prediction engine for machine learning model deployment. A service provider may provide AI hosting platforms that allow for clients, customers, and other end users to upload AI models for execution, such as machine learning models. A user may utilize one or more user interfaces provided to a client device by the service provider to select machine learning models to perform predictive services based on input features provided in an input string. Thereafter, a machine learning engine may host and execute the models during an instance of the engine provided to the client device. The engine may then process the input features in a processing thread remotely from the client device during the instance so that machine learning predictions may be determined. Thereafter, an output string for the predictions and model explanations may be provided to the client device.

Abstract: There are provided systems and methods for a shared prediction engine for machine learning model deployment. A service provider may provide AI hosting platforms that allow for clients, customers, and other end users to upload AI models for execution, such as machine learning models. A user may utilize one or more user interfaces provided to a client device by the service provider to select machine learning models to perform predictive services based on input features provided in an input string. Thereafter, a machine learning engine may host and execute the models during an instance of the engine provided to the client device. The engine may then process the input features in a processing thread remotely from the client device during the instance so that machine learning predictions may be determined. Thereafter, an output string for the predictions and model explanations may be provided to the client device.

Abstract: There are provided systems and methods for machine learning model verification for assessment pipeline deployment. A service provider may provide AI hosting platforms that allow for clients, customers, and other end users to upload AI models for execution, such as machine learning models. A user may utilize one or more user interfaces to provide model data and files, such as model artifacts, model requirements, and model test data. Thereafter, a model deployer may validate that the AI hosting platform has the required code packages and other model framework requirements for the AI model. The model test data may be used to ensure that the AI model is behaving correctly and providing the correct predictions based on input data and features. If so, the AI model may be deployed in a live production computing environment and used for predictive services.

Abstract: There are provided systems and methods for a shared prediction engine for machine learning model deployment. A service provider may provide AI hosting platforms that allow for clients, customers, and other end users to upload AI models for execution, such as machine learning models. A user may utilize one or more user interfaces provided to a client device by the service provider to select machine learning models to perform predictive services based on input features provided in an input string. Thereafter, a machine learning engine may host and execute the models during an instance of the engine provided to the client device. The engine may then process the input features in a processing thread remotely from the client device during the instance so that machine learning predictions may be determined. Thereafter, an output string for the predictions and model explanations may be provided to the client device.

Abstract: Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features.