A computing device , comprising: one or more processors ; and one or more non-transitory computer-readable media that store: one or more applications (120a-c) implemented by the one or more processors; a centralized example database that stores training examples received from the one or more applications, wherein the centralized example database is not directly accessible by the one or more applications (120a-c); 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 new training example from a first application of the one or more applications via a collection application programming interface; determining one or more context features descriptive of a context associated with the computing device; and storing the new training example together with the one or more context features in the centralized example database for use in training a machine-learned model stored by the computing device, such that the one or more context features are not accessible to the first application. one or more processors ; and one or more non-transitory computer-readable media that store: one or more applications (120a-c) implemented by the one or more processors; a centralized example database that stores training examples received from the one or more applications, wherein the centralized example database is not directly accessible by the one or more applications (120a-c); 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 new training example from a first application of the one or more applications via a collection application programming interface; determining one or more context features descriptive of a context associated with the computing device; and storing the new training example together with the one or more context features in the centralized example database for use in training a machine-learned model stored by the computing device, such that the one or more context features are not accessible to the first application. one or more applications (120a-c) implemented by the one or more processors; a centralized example database that stores training examples received from the one or more applications, wherein the centralized example database is not directly accessible by the one or more applications (120a-c); 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 new training example from a first application of the one or more applications via a collection application programming interface; determining one or more context features descriptive of a context associated with the computing device; and storing the new training example together with the one or more context features in the centralized example database for use in training a machine-learned model stored by the computing device, such that the one or more context features are not accessible to the first application. receiving a new training example from a first application of the one or more applications via a collection application programming interface; determining one or more context features descriptive of a context associated with the computing device; and storing the new training example together with the one or more context features in the centralized example database for use in training a machine-learned model stored by the computing device, such that the one or more context features are not accessible to the first application. <EOS>