Dell products l.p. (20240281661). DRIFT FORECASTING FOR ALTERNATIVE MODEL SELECTION simplified abstract
Contents
DRIFT FORECASTING FOR ALTERNATIVE MODEL SELECTION
Organization Name
Inventor(s)
Vinicius Michel Gottin of Rio de Janeiro (BR)
[[:Category:Herberth Birck Fr�hlich of Florianópolis (BR)|Herberth Birck Fr�hlich of Florianópolis (BR)]][[Category:Herberth Birck Fr�hlich of Florianópolis (BR)]]
Julia Drummond Noce of Rio de Janeiro (BR)
Ítalo Gomes Santana of Rio de Janeiro (BR)
DRIFT FORECASTING FOR ALTERNATIVE MODEL SELECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240281661 titled 'DRIFT FORECASTING FOR ALTERNATIVE MODEL SELECTION
Simplified Explanation: The patent application describes a method where a central node evaluates the performance of a reference model deployed at an edge node. If the evaluation exceeds a threshold, a counter is incremented. When the counter reaches a specified limit, an interpolation process is performed to identify a new model with better expected performance, which is then deployed in shadow mode at the edge node.
Key Features and Innovation:
- Evaluation of performance of a reference model at an edge node
- Incrementing a counter when performance exceeds a threshold
- Interpolation process to identify a new model with better expected performance
- Deployment of the new model in shadow mode at the edge node
Potential Applications: This technology could be applied in various industries such as telecommunications, IoT, and edge computing for optimizing model performance in real-time scenarios.
Problems Solved: This technology addresses the challenge of continuously improving model performance at edge nodes without disrupting operations.
Benefits:
- Enhanced performance of models deployed at edge nodes
- Real-time optimization of model performance
- Seamless deployment of new models without downtime
Commercial Applications: The technology could be utilized in industries such as autonomous vehicles, smart cities, and industrial automation for improving the efficiency and accuracy of edge computing systems.
Prior Art: Readers can explore prior art related to performance evaluation and model optimization in edge computing systems to gain a deeper understanding of the technological landscape.
Frequently Updated Research: Stay updated on the latest research in edge computing, model optimization, and performance evaluation to enhance the implementation of this technology.
Questions about Edge Computing Optimization: 1. How does this technology contribute to the advancement of edge computing systems? 2. What are the potential implications of deploying new models in shadow mode at edge nodes?
Original Abstract Submitted
one example method includes obtaining, by a central node, an evaluation of a performance of a reference model deployed at an edge node, determining if the evaluation exceeds a threshold associated with the reference model, and incrementing a counter when the evaluation exceeds the threshold, when a counter value equals or exceeds a specified limit, performing an interpolation process to identify a new model having better expected performance than performance of the reference model, and deploying the new model in a shadow mode at the edge node.
