Parallel development and deployment for machine learning models
US11468366B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 9, 2019 |
| Grant date | Oct 11, 2022 |
| Priority date | — |
| Expiry date | Dec 18, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/08
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined. A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.