Disclosed is a technique that can be performed by an electronic device. The technique can include generating raw data based on inputs to the electronic device, and sending the raw data or data items over a network to a server computer system. The sent raw data or the data items can include training data. The technique can further include receiving global model data from the server computer system over the network. The global model data may have been derived from the training data in accordance with a machine learning process. The technique can further include generating an updated local model by updating a local model associated with the electronic device based on the received global model data, and processing local data based on the updated local model to generate output data. The local data can include raw data or data items generated based on inputs to the electronic device.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: generating, by an electronic device, raw data based on inputs to the electronic device; sending, by the electronic device, the raw data or data derived from the raw data over a network to a server computer system, the sent raw data or the data derived from the raw data including training data; receiving, by the electronic device, model data from the server computer system over the network, the model data having been derived by the server computer system from the training data by use of a machine learning process; generating, by the electronic device, an updated local model by updating a local model associated with the electronic device based on the received model data; and processing, by the electronic device, local data based on the updated local model to generate output data, the local data including raw data or data derived from raw data generated based on inputs to the electronic device. 2. The method of claim 1, further comprising: executing, by the electronic device, a local action based on the output data, the local action including at least one of changing an operational parameter of the electronic device or changing a setting controlled by the electronic device. 3. The method of claim 1, further comprising, prior to sending the training data to the server computer system: generating, by the electronic device, a plurality of events indexed by timestamps, each of the plurality of events including a respective segment of the raw data; and extracting, by the electronic device, a data field from each of the plurality of events, the extracted data fields corresponding to the data derived from the raw data. 4. The method of claim 1, further comprising, prior to sending the training data to the server computer system: generating, by the electronic device, a plurality of events indexed by timestamps, each of the plurality of events including a respective segment of the raw data; and extracting, by the electronic device, after the plurality of events are indexed, a data field from each segment of raw data in each event, the extracted data fields corresponding to the data derived from the raw data. 5. The method of claim 1, further comprising, prior to sending the training data to the server computer system: generating, by the electronic device, a plurality of events indexed by timestamps, each of the plurality of events including a respective segment of the raw data; and extracting, by the electronic device, while the plurality of events are being indexed, a data field from each segment of raw data, the extracted data fields corresponding to the data derived from the raw data. 6. The method of claim 1, wherein the output data is first output data, the method further comprising: experiencing, by the electronic device, a termination of all communication links with the server computer system; generating, by the electronic device, a locally updated local model by performing a machine learning process to train the updated local model with raw data or data derived from raw data; processing, by the electronic device, local data in accordance with the locally updated local model while the electronic device is communicatively disconnected from the server computer system to generate second output data; and executing, by the electronic device, a local action based on the second output data, the local action including at least one of changing an operational parameter of the electronic device or changing a setting controlled by the electronic device. 7. The method of claim 1, wherein the model data is derived from at least one of raw data or data derived from raw data generated by a plurality of electronic devices including the electronic device, the model data having been derived in accordance with the machine learning process. 8. The method of claim 1, wherein the electronic device is an endpoint device that forms an endpoint of a communications link over the network. 9. The method of claim 1, wherein sending the raw data or the data derived from the raw data over the network to the server computer system comprises: establishing a machine-to-machine (M2M) communications link between the electronic device and the server computer system; and sending, by the electronic device, the raw data or the data derived from the raw data via the machine-to-machine communications link to the server computer system. 10. The method of claim 1, wherein the training data is only a portion of raw data or data derived from raw data generated by the electronic device. 11. The method of claim 1, wherein updating the local model comprises: replacing the local model with a new local model included in the model data. 12. The method of claim 1, wherein updating the local model comprises: modifying the local model with data indicating changes to the local model included in the model data. 13. A method comprising: collecting, by a server computer system, global data from a plurality of electronic devices via a network, the global data including at least one of raw data or data derived from raw data generated locally by each of the plurality of electronic devices; generating, by the server computer system, an updated global model by executing a machine learning process to train a global model based on the global data or data derived from the global data, the updated global model being configured to enable the plurality of electronic devices each to generate local data-driven outputs based on inputs to the electronic devices; and sending, by the server computer system, model data based on the updated global model to each electronic device of the plurality of electronic devices, to cause the electronic device to modify or replace the local model of the electronic device based on the model data. 14. The method of claim 13, further comprising, prior to executing the machine learning process: generating, by the server computer system, a plurality of events indexed by timestamps, each of the plurality of events including a respective segment of the global data; and extracting, by the server computer system, a data field from each of the plurality of events to obtain the data derived from the global data. 15. The method of claim 13, further comprising, prior to performing the machine learning process: generating, by the server computer system, a plurality of events indexed by timestamps, each of the plurality of events including a respective segment of the global data; and extracting, by the server computer system, after the plurality of events is indexed, a data field from each segment of global data in each event to obtain the data derived from the global data. 16. The method of claim 13, further comprising, prior to performing the machine learning process: generating, by the server computer system, a plurality of events indexed by timestamps, each of the plurality of events including a respective segment of the global data; and extracting, by the server computer system, while the plurality of events are being indexed, a data field from each segment of global data to obtain the data derived from the global data. 17. The method of claim 13, wherein the server computer system is intermittently communicatively connected to at least some of the plurality of electronic devices. 18. The method of claim 13, wherein the model data for each of the electronic devices includes an instance of the updated global model configured to cause the electronic device to replace the local model associated with the electronic device with the instance of the global model. 19. The method of claim 13, wherein the model data for each of the electronic devices includes data configured to cause the electronic device to modify the local model associated with the electronic device in accordance with the updated global model. 20. An electronic device comprising: a communication device through which the electronic device can communicate with a server computer system over a network; a processor; and memory containing instructions that, when executed by the electronic device, cause the electronic device to: generate raw data based on inputs to the electronic device; send the raw data or data derived from the raw data over a network to a server computer system, the sent raw data or the data derived from the raw data including training data; receive model data from the server computer system over the network, the model data having been derived by the server computer system from the training data by use of a machine learning process; generate an updated local model by updating a local model associated with the electronic device based on the received model data; and process local data based on the updated local model to generate output data, the local data including raw data or data derived from raw data generated based on inputs to the electronic device. 21. The electronic device of claim 20, wherein the instructions further cause the electronic device to: execute a local action based on the output data, the local action including at least one of changing an operational parameter of the electronic device or changing a setting controlled by the electronic device. 22. The electronic device of claim 20, wherein the instructions further cause the electronic device to, prior to sending the training data: generate a plurality of events indexed by timestamps, each of the plurality of events including a respective segment of the raw data; and extract, after the plurality of events are indexed, a data field from each segment of raw data in each event, the extracted data fields corresponding to the data derived from the raw data. 23. The electronic device of claim 20, wherein the output data is first output data, and the instructions further cause the electronic device to: experience termination of all communication links with the server computer system; generate a locally updated local model by performing a machine learning process to train the updated local model with raw data or data derived from raw data; process local data in accordance with the locally updated local model while the electronic device is communicatively disconnected from the server computer system to generate second output data; and execute a local action based on the second output data, the local action including at least one of changing an operational parameter of the electronic device or changing a setting controlled by the electronic device. 24. The electronic device of claim 20, wherein the model data is derived from raw data or data derived from raw data generated by a plurality of electronic devices including the electronic device, the model data having been derived in accordance with the machine learning process. 25. The electronic device of claim 20, wherein the electronic device is an endpoint device configured to form an endpoint of a communications link over the network. 26. The electronic device of claim 20, wherein the model data is configured to modify the local model or replace the local model with a new local model included in the model data. 27. A server computer system comprising: a communication device through which the server computer system can communicate with a plurality of electric devices over a network; a processor; and memory containing instructions that, when executed by the server computer system, cause the server computer system to: collect global data from the plurality of electronic devices via a network, the global data including at least one of raw data or data derived from raw data generated locally by each of the plurality of electronic devices; generate an updated global model by executing a machine learning process to train a global model based on the global data or data derived from the global data, the updated global model being configured to enable the plurality of electronic devices each to generate local data-driven outputs based on inputs to the electronic devices; and send model data based on the updated global model to each electronic device of the plurality of electronic devices, to cause the electronic device to modify or replace the local model of the electronic device based on the model data. 28. The server computer system of claim 27, wherein the instructions further cause the server computer system to: generate a plurality of events indexed by timestamps, each of the plurality of events including a respective segment of the global data; and extract, after the plurality of events is indexed, a data field from each segment of global data in each event to obtain the data derived from the global data. 29. The server computer system of claim 27, wherein the server computer system is intermittently communicatively connected to at least some of the plurality of electronic devices. 30. The server computer system of claim 27, wherein the model data for each of the electronic devices includes an instance of the updated global model configured to cause the electronic device to replace the local model associated with the instance of the global model.