Adaptive Analytics Retraining via Quality Feedback
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Solution Overview
Problem
Maintaining high-quality big-data analytics models is challenging due to continuously changing training data, as existing methods like incremental analytics can be costly and result in suboptimal quality when dealing with large datasets and model drift.
Innovation Solution
A quality-directed adaptive analytics retraining framework (AQuA) that incrementally evaluates model quality, retraining only when necessary, using strategies like sliding-window approaches and Exponential Moving Average to detect abrupt model drift, thereby maintaining model quality while minimizing training costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If incremental analytics is used to update the machine learning model, then the model can be updated continuously, but the training cost becomes expensive and model quality degrades
Solution Approach 1:
The system implements feedback by continuously evaluating model quality using a normalized quality measure after each incremental update. This feedback mechanism allows the system to detect when model quality degrades and trigger retraining only when necessary, rather than continuously or never updating. The quality evaluation creates a closed-loop control system that balances update frequency with resource consumption.
Solution Approach 2:
The system dynamically adjusts the retraining decision based on current model quality metrics. Instead of using a static update schedule or always performing incremental updates, the system adapts its behavior by evaluating whether the model quality has degraded below acceptable thresholds. This dynamic approach allows the system to optimize between continuous updates and cost efficiency by making retraining decisions based on actual model performance.
2Reliability
If the machine learning model is retrained frequently to maintain high quality, then model quality is improved, but training time and computational resources increase
Solution Approach 1:
The system uses feedback through normalized quality measures to determine when retraining is necessary. By continuously monitoring model quality and comparing it against thresholds, the system can maintain high reliability by triggering retraining only when quality degradation is detected, rather than retraining on a fixed schedule or continuously. This reduces unnecessary training time while maintaining model quality.
Solution Approach 2:
The system changes the parameter of retraining frequency based on model quality metrics. Instead of using a constant retraining interval, the system adjusts the effective retraining frequency by evaluating model quality and triggering retraining dynamically. This allows the system to maintain high model quality (reliability) while minimizing training time by avoiding redundant retraining operations when the model is still performing well.
3Loss of energy
If the machine learning model is not retrained to save resources, then training cost is reduced, but model quality degrades due to data drift
Solution Approach 1:
The system implements feedback through continuous quality evaluation to detect when model degradation occurs due to data drift. By monitoring normalized quality measures, the system can identify when retraining is necessary to maintain model quality, preventing the model from degrading while avoiding unnecessary retraining when the model is still effective. This resolves the contradiction by making retraining decisions based on actual model performance rather than fixed schedules.
Solution Approach 2:
The system performs preliminary evaluation of model quality using normalized quality measures before deciding whether to retrain. This preliminary assessment allows the system to predict when model quality will degrade and take preventive action (retraining) before significant degradation occurs. By evaluating quality metrics in advance, the system can maintain model reliability while optimizing training costs by retraining only when truly necessary.
4Reliability
If comprehensive model evaluation is performed to ensure quality, then model quality is maintained, but computational overhead increases
Solution Approach 1:
The system performs partial evaluation by using normalized quality measures that assess model performance without requiring complete re-evaluation of all training data. Instead of comprehensive retraining or full model validation, the system uses efficient quality metrics that provide sufficient information to determine whether retraining is needed. This partial action approach maintains model quality while reducing computational overhead compared to comprehensive evaluation methods.
Data Source
AI summary
A quality-directed adaptive analytic retraining is provided. Training example data with which to retrain a machine learning model that has been previously trained is received. The training example data is stored in a memory. The machine learning model is evaluated at least by running the machine learning model with the training example data. A normalized quality measure may be determined based on the evaluating. Whether to retrain the machine learning model is determined at least based on the normalized quality measure. Responsive to determining that the machine learning model is to be retrained, the machine learning model is retrained.