Computing system for training, deploying, executing, and updating machine learning models

A method executed by a processor of a server computing device, comprising:

• receiving an identity of a file, a timestamp, and a label from a developer computing device, wherein the label identifies a prediction that is to be made, wherein a target application is to utilize the prediction;
• generating training data based upon the identity of the file, the timestamp, and the label, wherein generating the training data comprises generating values for a predefined set of features;
• training a computer-implemented machine learning (ML) model based upon the training data, wherein the ML model is configured to take input values for client-generated features as input, wherein the ML model is configured to output the prediction based upon the input values;
• generating client configuration data that comprises transformations that are to be applied to values produced by client computing devices in order to generate the input values;
• deploying ML assets to the client computing devices, the ML assets comprising the ML model and the client configuration data, wherein the client computing devices transform the values produced by the client computing devices to the input values based upon the client configuration data, wherein the client computing devices provide the input values as input to the ML model, wherein the ML model outputs the prediction based upon the input values;
• and
• subsequent to deploying the ML assets to the client computing devices, updating the ML assets based upon telemetry data received from the client computing devices.

A computer-readable storage medium comprising instructions that, when executed by a processor, cause the processor to perform acts comprising:

• receiving an identity of a file that includes an identifier for a device, a timestamp, and a label from a developer computing device, wherein the label identifies a prediction that is to be made, where a target application is to utilize the prediction;
• generating training data based upon the identifier for the device, the timestamp, and the label, wherein generating the training data comprises generating values for a predefined set of features;
• training a computer-implemented machine learning (ML) model based upon the training data, wherein the ML model is configured to take input values for client-generated features as input, wherein the ML model is configured to output the prediction based upon the input values;
• generating client configuration data that comprises transformations that are to be applied to values produced by client computing devices in order to generate the input values;
• deploying ML assets to the client computing devices, the ML assets comprising the ML model and the client configuration data, wherein the client computing devices transform the values produced by the client computing devices to the input values based upon the client configuration data, wherein the client computing devices provide the input values as input to the NIL model, wherein the ML model outputs the prediction based upon the input values;
• and
• subsequent to deploying the ML assets to the client computing devices, updating the ML assets based upon telemetry data received from the client computing devices.