A computing device, comprising: one or more processors; and one or more non-transitory computer-readable media that store: a plurality of applications (120a-c) implemented by the one or more processors; a centralized example database that stores training examples received from the plurality of applications; a machine-learned model operable to provide inferences to the plurality of applications, and instructions that, when executed by the one or more processors, cause the computing device to implement an on-device machine learning platform that performs operations, the operations comprising: receiving a first training example from a first application of the plurality of applications via a collection application programming interface; storing the first training example in the centralized example database for use in training the machine-learned model such that the first training example is not accessible to the other applications of the plurality of applications; receiving input data from a second application of the plurality of applications via a prediction application programming interface; employing at least the machine-learned model to generate at least one inference based at least in part on the input data, wherein the computing device uses the inference to perform a type of task; and providing the at least one inference generated by the machine-learned model to the second application via the prediction application programming interface. one or more processors; and one or more non-transitory computer-readable media that store: a plurality of applications (120a-c) implemented by the one or more processors; a centralized example database that stores training examples received from the plurality of applications; a machine-learned model operable to provide inferences to the plurality of applications, and instructions that, when executed by the one or more processors, cause the computing device to implement an on-device machine learning platform that performs operations, the operations comprising: receiving a first training example from a first application of the plurality of applications via a collection application programming interface; storing the first training example in the centralized example database for use in training the machine-learned model such that the first training example is not accessible to the other applications of the plurality of applications; receiving input data from a second application of the plurality of applications via a prediction application programming interface; employing at least the machine-learned model to generate at least one inference based at least in part on the input data, wherein the computing device uses the inference to perform a type of task; and a plurality of applications (120a-c) implemented by the one or more processors; a centralized example database that stores training examples received from the plurality of applications; a machine-learned model operable to provide inferences to the plurality of applications, and instructions that, when executed by the one or more processors, cause the computing device to implement an on-device machine learning platform that performs operations, the operations comprising: receiving a first training example from a first application of the plurality of applications via a collection application programming interface; storing the first training example in the centralized example database for use in training the machine-learned model such that the first training example is not accessible to the other applications of the plurality of applications; receiving input data from a second application of the plurality of applications via a prediction application programming interface; employing at least the machine-learned model to generate at least one inference based at least in part on the input data, wherein the computing device uses the inference to perform a type of task; and receiving a first training example from a first application of the plurality of applications via a collection application programming interface; storing the first training example in the centralized example database for use in training the machine-learned model such that the first training example is not accessible to the other applications of the plurality of applications; receiving input data from a second application of the plurality of applications via a prediction application programming interface; employing at least the machine-learned model to generate at least one inference based at least in part on the input data, wherein the computing device uses the inference to perform a type of task; and providing the at least one inference generated by the machine-learned model to the second application via the prediction application programming interface.