Abstract: Methods, systems, and computer-readable media for unified code and data management for machine learning models are disclosed. A plurality of dependency graphs, including a first dependency graph and a second dependency graph, are generated. The graphs comprise nodes associated with a software development model or machine learning model, and the nodes represent transforms. One or more transforms of the first dependency graph are used to generate first output corresponding to a node in the second dependency graph. One or more transforms of the second dependency graph are used to generate second output based at least in part on the first output.

Abstract: The security or other attributes of mobile applications may be assessed and assigned a security score. In one implementation, a device may obtain information relating to the mobile applications, and may determine, for each of the mobile applications, a number of security scores. Each of the security scores may define a level of risk for a security category relating to a mobile application. The device may further combine the security scores, for each of the mobile applications, to obtain, for each of the mobile applications, a final security score.

Abstract: The security or other attributes of mobile applications may be assessed and assigned a security score. In one implementation, a device may obtain information relating to the mobile applications, and may determine, for each of the mobile applications, a number of security scores. Each of the security scores may define a level of risk for a security category relating to a mobile application. The device may further combine the security scores, for each of the mobile applications, to obtain, for each of the mobile applications, a final security score.

Abstract: The security or other attributes of mobile applications may be assessed and assigned a security score. In one implementation, a device may obtain information relating to the mobile applications, and may determine, for each of the mobile applications, a number of security scores. Each of the security scores may define a level of risk for a security category relating to a mobile application. The device may further combine the security scores, for each of the mobile applications, to obtain, for each of the mobile applications, a final security score.

Abstract: The security or other attributes of mobile applications may be assessed and assigned a security score. In one implementation, a device may obtain information relating to the mobile applications, and may determine, for each of the mobile applications, a number of security scores. Each of the security scores may define a level of risk for a security category relating to a mobile application. The device may further combine the security scores, for each of the mobile applications, to obtain, for each of the mobile applications, a final security score.

Abstract: A system for processing credit applications. An adaptive decision engine produces a score determined by the risk associated with a credit application. The credit application is automatically approved or rejected if the score is above or below certain thresholds, and is otherwise referred for manual processing. The system may include a rules system that automatically approves applications if all conditions are met, and generates exceptions if all conditions are not met and refers the application to the adaptive decision engine for further processing. A decision to approve or reject an application may be based on a combination of the risk score and number of exceptions.