USING A CLIENT-SIDE MACHINE LEARNING MODEL DYNAMICALLY IN A MOBILE ENTERPRISE APPLICATION ON A MOBILE DEVICE

A computer-implemented method for enabling a mobile enterprise application of a database system to use a machine learning (ML) service comprises modifying, by a provider of the database system, a mobile enterprise application made available to one or more tenants of the database system to include a native code layer capable of downloading and invoking a trained ML model. One or more servers of the database system receives an upload of the trained ML model and configuration data from a tenant user, wherein the configuration data defines a format of input data and a format of output data of the trained ML model. The one or more servers download the trained ML model to the mobile enterprise application running on a mobile device in response to a request by the native code layer of the mobile enterprise application. During execution of the mobile enterprise application on the mobile device, the native code layer accesses the trained ML model with specific input data local on the mobile device in the defined input format, and receives specific output data in the defined output format from the trained ML model.