Abstract: A computer-implemented method comprises receiving by a servers of a development platform, an upload of a trained ML model and configuration data from a tenant user, for use by a mobile enterprise application that is made available to one or more tenants of the development platform, the mobile enterprise application capable of invoking the trained ML model, wherein the configuration data defines an input data format and an output data format of the trained ML model. The server generates API based on the configuration data and downloading the API to the mobile enterprise application, wherein the generating the API is performed without using an API provided by the tenant user. The generated API is further configured to pass input in the input data format to the trained ML model and configured to receive output in the output data format from the trained ML model for further processing or display.

Abstract: A computer-implemented method comprises receiving by a servers of a development platform, an upload of a trained ML model and configuration data from a tenant user, for use by a mobile enterprise application that is made available to one or more tenants of the development platform, the mobile enterprise application capable of invoking the trained ML model, wherein the configuration data defines an input data format and an output data format of the trained ML model, The server generates API based on the configuration data and downloading the API to the mobile enterprise application, wherein the generating the API is performed without using an API provided by the tenant user. The generated API is further configured to pass input in the input data format to the trained ML model and configured to receive output in the output data format from the trained ML model for further processing or display.

Abstract: 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.

Abstract: 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.