Abstract: A non-transitory computer readable medium includes computer executable instructions that, when executed, cause at least one processor to train a model to perform at least one of a prediction operation, a diagnostic operation, or a classification operation based on a training dataset, deploy the model in a production computer system to perform the at least one operation on field data, monitor signal data associated with the model, the signal data including specific or derived signal data representing characteristics of an ecosystem in which the model is deployed and new observations in incoming field data, monitor accuracy of the model by applying a statistical tool to a plurality of data points of the signal data, apply a secondary machine learning predictive engine to the plurality of data points of the signal data to predict future data points of the signal data, determine whether the signal data represents an unstable process by identifying future outlier data points from among the plurality of future data

Abstract: An example system to obscure personally identifiable information of a user of an electronic device is disclosed. The example system includes a first processor to select a set of instructions from a plurality of sets of instructions. The first processor also is to transmit the set of instructions to a second processor, the second processor disposed in an electronic device, the electronic device remote from the first processor, the set of instructions to cause the second processor to obtain non-personally identifiable data from the personally identifiable information gathered by the electronic device. The example system also includes a remote data reader to read the non-personally identifiable data from the electronic device.

Abstract: A non-transitory computer readable medium includes computer executable instructions that, when executed, cause at least one processor to train a model to perform at least one of a prediction operation, a diagnostic operation, or a classification operation based on a training dataset, deploy the model in a production computer system to perform the at least one operation on field data, monitor signal data associated with the model, the signal data including specific or derived signal data representing characteristics of an ecosystem in which the model is deployed and new observations in incoming field data, monitor accuracy of the model by applying a statistical tool to a plurality of data points of the signal data, apply a secondary machine learning predictive engine to the plurality of data points of the signal data to predict future data points of the signal data, determine whether the signal data represents an unstable process by identifying future outlier data points from among the plurality of future data

Abstract: A method for automated assessment of a model includes: training a model to perform a prediction, diagnostic, or classification operation based on a training dataset; deploying the model in production to perform the operation on field data; monitoring signal data associated with the model automatically, the signal data including specific or derived signal data representing characteristics of an ecosystem in which the model is deployed and new observations in incoming field data; monitoring accuracy of the model by applying a statistical tool to a plurality of data points of the signal data; determining whether the signal data represents an unstable process by identifying outlier data points from among the plurality of data points of the signal data; generating an indication that a corrective action should be taken on the model based on a result of the determination; and displaying the indication on a display.

Abstract: An example system to obscure personally identifiable information of a user of an electronic device is disclosed. The example system includes a first processor to select a set of instructions from a plurality of sets of instructions. The first processor also is to transmit the set of instructions to a second processor, the second processor disposed in an electronic device, the electronic device remote from the first processor, the set of instructions to cause the second processor to obtain non-personally identifiable data from the personally identifiable information gathered by the electronic device. The example system also includes a remote data reader to read the non-personally identifiable data from the electronic device.

Abstract: Protecting personally identifiable information data collected and/or stored in physical objects with embedded electronic devices by performing at least the following: obtaining a plurality of personally identifiable information algorithms for a plurality of electronic user devices, determining a relevant personally identifiable information algorithm from the plurality of personally identifiable information algorithms, executing the relevant personally identifiable information algorithm over the relevant personally identifiable information from one of the electronic user devices to construct a personally identifiable information data result, and transmitting the personally identifiable information data result without transmitting the relevant personally identifiable information to a remote computing system.

Abstract: A method for automated assessment of a model includes: training a model to perform a prediction, diagnostic, or classification operation based on a training dataset; deploying the model in production to perform the operation on field data; monitoring signal data associated with the model automatically, the signal data including specific or derived signal data representing characteristics of an ecosystem in which the model is deployed and new observations in incoming field data; monitoring accuracy of the model by applying a statistical tool to a plurality of data points of the signal data; determining whether the signal data represents an unstable process by identifying outlier data points from among the plurality of data points of the signal data; generating an indication that a corrective action should be taken on the model based on a result of the determination; and displaying the indication on a display.

Abstract: Protecting personally identifiable information data collected and/or stored in physical objects with embedded electronic devices by performing at least the following: obtaining a plurality of personally identifiable information algorithms for a plurality of electronic user devices, determining a relevant personally identifiable information algorithm from the plurality of personally identifiable information algorithms, executing the relevant personally identifiable information algorithm over the relevant personally identifiable information from one of the electronic user devices to construct a personally identifiable information data result, and transmitting the personally identifiable information data result without transmitting the relevant personally identifiable information to a remote computing system.