ML Model Degradation Detection via Accuracy KPIs
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Solution Overview
Problem
Machine learning models deployed in production environments often deteriorate due to deviations in input datasets from their training datasets, leading to performance degradation, which existing technologies fail to effectively detect and address.
Innovation Solution
A system and method that includes a machine learning model controller to detect degradation by using accuracy key performance indicators such as prediction power and confidence metrics, as well as user feedback, and retrain the model using a new dataset that includes samples not part of the original training dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model is trained on a fixed training dataset and deployed in production, then the model can perform cognitive tasks such as text classification and priority assignment, but the model deteriorates over time due to deviations in input datasets from the training dataset, leading to performance degradation
Solution Approach 1:
The system implements continuous monitoring of model performance using accuracy key performance indicators (KPIs) such as prediction power metrics and prediction confidence metrics. When degradation is detected through these feedback mechanisms, the system automatically triggers retraining processes. This closed-loop feedback system enables the model to adapt to distribution shifts in input data while maintaining reliable performance, resolving the contradiction between initial model reliability and long-term adaptability.
Solution Approach 2:
The patent transforms the static machine learning model into a dynamic system that can adapt its parameters and structure over time. By implementing continuous monitoring of performance KPIs and automated retraining mechanisms, the model evolves to match changing input data distributions. This dynamic approach allows the model to maintain both reliability through performance monitoring and adaptability through continuous updates, directly addressing the technical contradiction.
2Reliability
If the machine learning model is continuously monitored using accuracy key performance indicators, then performance degradation can be detected, but this requires additional computational resources and complexity for monitoring and retraining operations
Solution Approach 1:
The system implements self-service mechanisms where the machine learning model monitors its own performance through accuracy KPIs and automatically triggers retraining when degradation is detected. The model serves itself by identifying performance issues and initiating corrective actions without external intervention. This self-service approach reduces the need for complex external monitoring infrastructure while maintaining reliable performance detection, resolving the contradiction between monitoring reliability and system complexity.
Solution Approach 2:
The patent employs feedback loops where performance metrics are continuously measured and fed back to control retraining operations. When accuracy KPIs indicate degradation, the feedback mechanism automatically initiates retraining with new data. This feedback-driven approach simplifies the monitoring system by using the model's own performance data as the trigger for complex retraining operations, balancing monitoring reliability with acceptable system complexity.
3Reliability
If the machine learning model is retrained using a new training dataset that includes samples not in the original dataset, then predictive power and confidence are enhanced, but this requires additional data collection and processing time
Solution Approach 1:
The system performs preliminary actions by continuously collecting and preparing new training data in advance before it is strictly needed. The monitoring of accuracy KPIs allows the system to proactively gather additional training samples and prepare retraining datasets before performance degradation becomes critical. This preliminary data collection and preparation reduces the actual retraining time and minimizes service interruptions, resolving the contradiction between improved predictive power and time loss.
Solution Approach 2:
The patent implements dynamic retraining schedules where the timing and frequency of retraining operations are adjusted based on actual performance degradation rates. When the model shows signs of deterioration through monitored KPIs, retraining is dynamically triggered with freshly collected data. This dynamic approach ensures that retraining occurs at optimal moments with sufficient new data available, balancing the need for enhanced predictive confidence against the time required for data collection and processing.
Data Source
AI summary
A method may include training, based on a first training dataset, a machine learning model. A degradation of the machine learning model may be detected based on one or more accuracy key performance indicators including a prediction power metric and a prediction confidence metric. The degradation of the machine learning model may also be detected based on a drift and skew in an input dataset and/or an output dataset of the machine learning model. Furthermore, the degradation of the machine learning model may be detected based on an explicit feedback and/or an implicit feedback on a performance of the machine learning model. In response to detecting the degradation of the machine learning model, the machine learning model may be retrained based on a second training dataset that includes at least one training sample not included in the first training dataset. Related systems and articles of manufacture are also provided.