Sap Se

Xiaoqing He

Jing Ye

Yann Le Biannic

Prerna Makanawala

Prajesh K

Karthik S J

Amy He

Georg Glantschnig

Subhobrata Dey

Riya Thosar

Arjun Karat

A method of improving usability and transparency of machine-learning aspects of applications providing various types of services is disclosed. Based on a request submitted through an administrative user interface, a data readiness check is performed on underlying data associated with the application. Based on a successful completion of the data readiness check, a configuration file is retrieved from an application server. The configuration file specifies a plurality of keys for generating a machine-learned model for the application. The machine-learned model is trained based on the plurality of keys specified in the configuration file. The machine-learned model is selected from a plurality of machine-learned models based on dry runs of the each of the plurality of models. The machine-learned model is activated with respect to the application. Scores are identified from the underlying data items based on the selected machine-learned model.

Applying scoring systems using an auto-machine learning classification approach

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Palo Alto, CA, United States of America

Xiaoqing He