Adaptive ML Model Training via Live Metrics
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Solution Overview
Problem
Machine learning models in network assurance systems face resource-intensive training processes and risk of overfitting, leading to poor performance and increased resource consumption, especially when dealing with large datasets and evolving network conditions.
Innovation Solution
Implement an adaptive training mechanism that deploys a second machine learning model to replace a poorly performing first model, using live performance metrics to identify corrective measures that minimize resource consumption, such as incremental retraining or selecting existing models with matching input data distributions, and leveraging a model dependency inferrer to manage dependencies among models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained using large datasets and complex GPU-based infrastructure, then model performance and accuracy are improved, but resource consumption and training time increase significantly
Solution Approach 1:
The patent applies partial action by performing incremental retraining that updates only a portion of the model parameters rather than complete retraining. This allows the system to maintain model performance while significantly reducing the computational resources and time required, as the model is updated periodically with new data rather than being fully retrained on the entire dataset
Solution Approach 2:
The system performs preliminary actions by proactively detecting performance degradation through live metrics monitoring and triggering retraining before the model becomes completely obsolete. This allows the system to maintain optimal performance with minimal retraining interventions rather than continuous full retraining
2Adaptability or versatility
If machine learning models are retrained frequently to adapt to evolving network conditions, then model adaptability is improved, but resource consumption and risk of overfitting increase
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring live performance metrics and using this information to trigger retraining only when performance degradation is detected. This feedback-driven approach ensures the model adapts to changing network conditions while avoiding unnecessary retraining that would consume resources and potentially cause overfitting
Solution Approach 2:
The system dynamically adjusts the retraining frequency and scope based on actual performance needs rather than following a fixed schedule. The incremental retraining process adapts the model progressively to new conditions, allowing the system to balance adaptability with resource conservation by retraining only as much as necessary
3Reliability
If complete model retraining is performed to address performance degradation, then model performance is restored, but training time and resource consumption increase
Solution Approach 1:
The patent applies partial action through incremental retraining that updates only the necessary portions of the model based on performance degradation detection. This restores model performance by addressing specific issues with new data while avoiding the time and resource costs of complete retraining on the entire dataset
Data Source
AI summary
In one embodiment, a device deploys a first machine learning model to an inference location in a network. The first machine learning model is used at the inference location to make inferences about the network. The device receives, from the inference location, an indication that the first machine learning model is exhibiting poor performance. The device identifies a corrective measure for the poor performance that minimizes resource consumption by a model training pipeline of the device. The device deploys, based on the corrective measure, a second machine learning model to the inference location. The second machine learning model is used in lieu of the first machine learning model to make the inferences about the network.