ML Model Performance Anomaly Detection in MLOps
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Solution Overview
Problem
Machine learning-based network assurance systems face challenges in distinguishing between actual network issues and performance issues attributed to the machine learning model or its training data, leading to unnecessary corrective measures that can impact network performance.
Innovation Solution
A unified methodology and architecture for anomaly detection in machine learning model performance, which tracks performance metrics over time, identifies degradation anomalies, and initiates corrective measures by correlating training and inference metrics, and data distributions across networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning-based systems are used to monitor network health, then the ability to analyze complex network patterns and predict problems is improved, but the complexity of the system increases and it becomes difficult to distinguish between actual network issues and model performance issues
Solution Approach 1:
The system segments the monitoring function into two independent components: a primary machine learning model for network anomaly detection and a secondary performance monitoring model for detecting model degradation. This segmentation allows each model to specialize in its function while maintaining overall system manageability and clarity in distinguishing between network issues and model performance issues.
Solution Approach 2:
The performance monitoring model acts as an intermediary between the primary monitoring system and the network operations team. It monitors the primary model's performance metrics and detects when the model itself is experiencing issues, thereby mediating the distinction between actual network problems and model performance degradation before corrective actions are taken.
2Productivity
If corrective measures are initiated based on model detections, then network problems can be addressed, but unnecessary corrective measures may be initiated when the model performance is degraded
Solution Approach 1:
The system performs preliminary monitoring of model performance metrics before initiating corrective actions. The performance monitoring model continuously assesses whether the primary model is functioning correctly, and only allows corrective actions to proceed when model performance is confirmed to be within acceptable thresholds, thereby preventing premature or incorrect interventions.
Solution Approach 2:
The system implements a feedback mechanism where the performance monitoring model continuously evaluates the primary model's performance and provides feedback on model health status. This feedback loop ensures that corrective actions are only initiated when both network anomalies and model performance issues are properly differentiated, improving the reliability of automated responses.
Data Source
AI summary
In one embodiment, a service tracks performance of a machine learning model over time. The machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks. The service also tracks performance metrics associated with training of the machine learning model. The service determines that a degradation of the performance of the machine learning model is anomalous, based on the tracked performance of the machine learning model and performance metrics associated with training of the model. The service initiates a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.