Handling system-characteristics drift in machine learning applications
US11934927B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 22, 2022 |
| Grant date | Mar 19, 2024 |
| Priority date | — |
| Expiry date | Dec 22, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/24
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for managing input and output error of a machine learning (ML) model in a database system are presented herein. A set of test queries is executed on a first version of a database system to generate first test data, wherein the first version of the system comprises a ML model to generate an output corresponding to a function of the database system. An error model is trained based on the first test data and second test data generated based on a previous version of the system. The error model determines an error associated with the ML model between the first and previous versions of the system. The first version of the system is deployed with the error model, which corrects an output or an input of the ML model until sufficient data has been produced by the error model to retrain the ML model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.