Automatically predicting device failure using machine learning techniques
Methods, apparatus, and processor-readable storage media for automatically predicting device failure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from at least one client device; predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques; predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.
1. A computer-implemented method comprising:
obtaining telemetry data from at least one client device;
predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques, wherein processing at least a portion of the telemetry data using the first set of one or more machine learning techniques comprises processing at least a portion of the telemetry data using one or more Bayes classifier algorithms in conjunction with one or more probabilistic supervised machine learning algorithms;
predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and
performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
2. The computer-implemented method of claim 1 , wherein processing the at least a portion of the telemetry data using the first set of one or more machine learning techniques comprises processing the at least a portion of the telemetry data using one or more Naïve Bayes classifier algorithms.
3. The computer-implemented method of claim 2 , further comprising:
training the one or more Naïve Bayes classifier algorithms using historical telemetry data, device-related data, environmental data, and utilization information.
4. The computer-implemented method of claim 1 , further comprising:
training the one or more probabilistic supervised machine learning algorithms using historical telemetry data, device-related data, environmental data, and utilization information.
5. The computer-implemented method of claim 1 , further comprising:
determining a probability value attributed to the predicted failure based at least in part on the processing of the at least a portion of the telemetry data using the first set of one or more machine learning techniques.
6. The computer-implemented method of claim 1 , wherein processing the predicted failure and at least a portion of the telemetry data using the second set of one or more machine learning techniques comprises processing the predicted failure and at least a portion of the telemetry data using at least one gradient boosting regression technique.
7. The computer-implemented method of claim 6 , further comprising:
training the at least one gradient boosting regression technique using historical telemetry data, device-related data, environmental data, and utilization information.
8. The computer-implemented method of claim 1 , wherein performing the at least one automated action comprises outputting at least one notification pertaining to the predicted failure to at least one customer relationship management system.
9. The computer-implemented method of claim 1 , wherein performing the at least one automated action comprises:
determining a dispatch date pertaining to at least a portion of the at least one client device based at least in part on the predicted lifespan information; and
outputting the determined dispatch date to one or more of a parts planning entity, a services planning entity, a warranty entity, and a service level agreement entity.
10. The computer-implemented method of claim 1 , wherein performing the at least one automated action comprises:
modifying an existing dispatch date pertaining to at least a portion of the at least one client device based at least in part on the predicted lifespan information; and
outputting the modified dispatch date to one or more of a parts planning entity, a services planning entity, a warranty entity, and a service level agreement entity.
11. A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:
to obtain telemetry data from at least one client device;
to predict failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques, wherein processing at least a portion of the telemetry data using the first set of one or more machine learning techniques comprises processing at least a portion of the telemetry data using one or more Bayes classifier algorithms in conjunction with one or more probabilistic supervised machine learning algorithms;
to predict lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and
to perform at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.
12. The non-transitory processor-readable storage medium of claim 11 , wherein processing the at least a portion of the telemetry data using the first set of one or more machine learning techniques comprises processing the at least a portion of the telemetry data using one or more Naïve Bayes classifier algorithms.
13. The non-transitory processor-readable storage medium of claim 11 , wherein processing the predicted failure and at least a portion of the telemetry data using the second set of one or more machine learning techniques comprises processing the predicted failure and at least a portion of the telemetry data using at least one gradient boosting regression technique.
14. The non-transitory processor-readable storage medium of claim 11 , wherein the program code when executed by at least one processing device further causes the at least one processing device:
to determine a probability value attributed to the predicted failure based at least in part on the processing of the at least a portion of the telemetry data using the first set of one or more machine learning techniques.
15. An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured:
to obtain telemetry data from at least one client device;
to predict failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques, wherein processing at least a portion of the telemetry data using the first set of one or more machine learning techniques comprises processing at least a portion of the telemetry data using one or more Bayes classifier algorithms in conjunction with one or more probabilistic supervised machine learning algorithms;
to predict lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and
to perform at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.
16. The apparatus of claim 15 , wherein processing the at least a portion of the telemetry data using the first set of one or more machine learning techniques comprises processing the at least a portion of the telemetry data using one or more Naïve Bayes classifier algorithms.
17. The apparatus of claim 15 , wherein processing the predicted failure and at least a portion of the telemetry data using the second set of one or more machine learning techniques comprises processing the predicted failure and at least a portion of the telemetry data using at least one gradient boosting regression technique.
18. The apparatus of claim 15 , wherein performing the at least one automated action comprises outputting at least one notification pertaining to the predicted failure to at least one customer relationship management system.
19. The apparatus of claim 15 , wherein performing the at least one automated action comprises:
determining a dispatch date pertaining to at least a portion of the at least one client device based at least in part on the predicted lifespan information; and
outputting the determined dispatch date to one or more of a parts planning entity, a services planning entity, a warranty entity, and a service level agreement entity.
20. The apparatus of claim 15 , wherein performing the at least one automated action comprises:
modifying an existing dispatch date pertaining to at least a portion of the at least one client device based at least in part on the predicted lifespan information; and
outputting the modified dispatch date to one or more of a parts planning entity, a services planning entity, a warranty entity, and a service level agreement entity.