International business machines corporation (20240095547). DETECTING AND RECTIFYING MODEL DRIFT USING GOVERNANCE simplified abstract
Contents
- 1 DETECTING AND RECTIFYING MODEL DRIFT USING GOVERNANCE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DETECTING AND RECTIFYING MODEL DRIFT USING GOVERNANCE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
DETECTING AND RECTIFYING MODEL DRIFT USING GOVERNANCE
Organization Name
international business machines corporation
Inventor(s)
Neerju Gupta of Chelmsford MA (US)
Yannick Saillet of Stuttgart (DE)
DETECTING AND RECTIFYING MODEL DRIFT USING GOVERNANCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095547 titled 'DETECTING AND RECTIFYING MODEL DRIFT USING GOVERNANCE
Simplified Explanation
The embodiment described in the abstract is a system for monitoring machine learning models to detect and rectify model drift using governance. It involves registering multiple machine learning models to a governance dashboard, monitoring these models to identify factors and generate clusters of similar models, detecting incorrect decisions made by a target model, calculating correlation scores between the target model and others in the same cluster, and providing cluster reinforcement recommendations when a correlation score exceeds a threshold.
- The system registers multiple machine learning models to a governance dashboard.
- It monitors these models to identify factors and generate clusters of similar models.
- It detects incorrect decisions made by a target model.
- It calculates correlation scores between the target model and others in the same cluster.
- It provides cluster reinforcement recommendations when a correlation score exceeds a threshold.
Potential Applications
The technology described can be applied in various industries such as finance, healthcare, e-commerce, and more where machine learning models are used for decision-making processes.
Problems Solved
This technology helps in detecting and rectifying model drift in machine learning models, ensuring that they continue to make accurate decisions over time.
Benefits
The system provides a proactive approach to monitoring machine learning models, helping to maintain their performance and reliability by detecting and addressing model drift.
Potential Commercial Applications
The system can be utilized by companies that heavily rely on machine learning models for their operations to ensure the accuracy and consistency of their decision-making processes.
Possible Prior Art
One possible prior art could be systems that monitor machine learning models for performance metrics but do not specifically focus on detecting and rectifying model drift using governance.
Unanswered Questions
How does the system handle different types of machine learning models within the same cluster?
The system may need to adapt its monitoring and reinforcement recommendations based on the specific characteristics and requirements of each type of machine learning model.
What are the potential challenges in implementing this system in real-world applications?
Some challenges could include integrating the system with existing machine learning infrastructure, ensuring data privacy and security, and managing the computational resources required for monitoring multiple models simultaneously.
Original Abstract Submitted
an embodiment for monitoring machine learning models to detect and rectify model drift using governance. the embodiment may receive a plurality of machine learning models and register the plurality of machine learning models to a governance dashboard. the embodiment may automatically monitor the received plurality of machine learning models to identify factors used by each of the received plurality of machine learning models and generate corresponding clusters of similar machine learning models. the embodiment may automatically detect an incorrect decision made by a target machine learning model and then automatically calculate a correlation score between the target machine learning model and machine learning models within an associated corresponding cluster of similar machine learning models. the embodiment may, in response to detecting a correlation score above a threshold, automatically determine and output a cluster reinforcement recommendation.
