ML Model Retraining Triggers for Intermittent Adverse Event Data
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Solution Overview
Problem
Existing ML models for predicting adverse events, such as natural disasters, suffer from accuracy degradation due to model drift, leading to inefficient resource consumption and incremental improvements without significant accuracy gains.
Innovation Solution
A ML model management system that selectively updates or retrain models based on trigger decisions from data statistics, data ratio, decision points, and climatological signals, ensuring non-incremental improvements in model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are frequently updated or retrained to maintain prediction accuracy, then model accuracy is improved, but computational resources and time are excessively consumed
Solution Approach 1:
The system implements a feedback mechanism by continuously monitoring data characteristics and model performance metrics. A trigger module evaluates whether updating the model is necessary based on computed signals from data drift detection and performance degradation analysis, rather than following a fixed update schedule. This feedback loop ensures models are updated only when accuracy deterioration is detected, optimizing the balance between maintaining reliability and conserving computational resources.
Solution Approach 2:
The patent transitions from static, periodic model updating to a dynamic, condition-based updating approach. The system adapts its updating frequency and timing based on real-time assessment of data characteristics and model performance. This dynamic strategy allows the system to respond to actual needs, updating models more frequently during periods of high data drift and less frequently when stability is maintained, thereby optimizing resource utilization while preserving accuracy.
2Adaptability or versatility
If machine learning models are frequently retrained to adapt to changing data patterns, then adaptability is improved, but training time and computational burden increase
Solution Approach 1:
The system performs preliminary assessment of data characteristics and model performance before initiating model updates. By pre-computing signals related to data drift and performance degradation, the system can determine in advance whether retraining is necessary, avoiding unnecessary training operations and reducing overall training time while maintaining adaptability to genuine changes in data patterns.
Solution Approach 2:
The patent changes the parameter of model updating from fixed time intervals to condition-based triggers. By monitoring parameters such as data distribution shifts and performance metric degradation, the system dynamically adjusts when retraining occurs, ensuring adaptability to changing patterns while minimizing unnecessary training operations and associated time costs.
3Stability of the object's composition
If model updates are performed on fixed schedules, then consistency in maintenance is improved, but resource efficiency deteriorates due to unnecessary updates
Solution Approach 1:
The system replaces fixed-schedule maintenance with feedback-driven maintenance. Continuous monitoring of data characteristics and model performance provides real-time feedback that triggers updates only when necessary. This approach maintains consistency in the sense that the system consistently evaluates model health, but eliminates wasteful resource expenditure on updates when the model is performing adequately.
Solution Approach 2:
The patent transitions from static, predetermined maintenance schedules to dynamic, condition-based maintenance. The updating frequency and timing are continuously adjusted based on actual model performance and data characteristics, optimizing resource efficiency while maintaining appropriate model health through adaptive rather than rigid scheduling.
Data Source
AI summary
Methods, systems, and apparatus for providing a ML model for inference, the ML model having been trained using a first set of training data to provide predictions associated with an adverse event, after training of the ML model, receiving data from one or more data sources, the data representative of characteristics relevant to predictions associated with the adverse event, providing a second set of training data, determining, by a trigger module, a trigger decision based on a set of signals at least partially determined from the second set of training data, the trigger decision indicating whether the ML model is to be one of updated and retrained based on the second set of training data, and selectively executing one of updating and retraining of the ML model using at least a portion of the second set of training data in response to the trigger decision.