Systems and methods to execute efficiently a plurality of machine learning processes
US10671916B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 20, 2016 |
| Grant date | Jun 2, 2020 |
| Priority date | — |
| Expiry date | Jul 13, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2209/509
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Described herein are systems and methods for executing efficiently, in real-time, a plurality of machine learning processes. In one embodiment, a computing platform with multiple compute elements receives multiple data streams, each such stream associated with its own respective machine learning process. Each machine learning process is operative to use its data stream as input to train, in real-time, a respective mathematical model. Each of the processes has peaks and dips in processing demands. The system re-allocates, in real-time, compute elements from the processes with lower processing demands to processes with higher processing demands, thereby handling all of the multiple processes on-the-fly, preventing peak demands from causing the system to stall, and reducing overall the computational resources required by the system.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.