ML Model Anomaly Detection via Input-Output Causal Analysis
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Solution Overview
Problem
Conventional machine learning systems lack effective monitoring and fail to identify the causal link between input data anomalies and output performance issues, leading to unnecessary resource consumption and prolonged downtime.
Innovation Solution
The system actively monitors inputs, outputs, and performance of deployed machine learning models to detect anomalies and determine the causal relationship between input data anomalies and output performance issues, allowing for targeted corrections without retraining the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning systems continuously monitor and retrain models upon anomaly detection, then model performance is maintained, but computing and energy resources are significantly consumed
Solution Approach 1:
The patent introduces an intermediary analysis layer that sits between anomaly detection and model retraining. This intermediary component performs root cause analysis to determine whether detected anomalies are due to input data issues or actual model performance degradation. Only when the model itself is identified as the source of the problem does retraining occur, thereby avoiding unnecessary retraining cycles and reducing computing and energy resource consumption while maintaining model performance reliability
Solution Approach 2:
The system implements a feedback mechanism that continuously monitors both input data quality and model output performance. When anomalies are detected, the feedback loop triggers an analysis process that determines the source of the anomaly. This feedback-driven approach ensures that retraining is only initiated when actually necessary, optimizing the balance between maintaining model reliability and conserving computing resources
2Manufacturing precision
If machine learning systems retrain models frequently upon anomaly detection, then model accuracy is maintained, but downtime increases
Solution Approach 1:
The system performs preliminary root cause analysis before initiating model retraining. By proactively identifying whether the anomaly stems from input data issues or model degradation, the system可以避免 unnecessary retraining operations. This preliminary action ensures that retraining is only triggered when actually needed, thereby maintaining model accuracy while minimizing the time the model is taken offline for retraining
Solution Approach 2:
The patent extracts and separates the root cause analysis function from the model retraining process. By taking out the diagnostic step and making it an independent preliminary operation, the system can identify and address input data issues without triggering model retraining. This separation ensures that model accuracy is maintained through targeted interventions while minimizing downtime by avoiding unnecessary retraining cycles
3Measurement precision
If conventional systems monitor model performance, then performance degradation is detected, but the causal link between input anomalies and output issues remains unidentified
Solution Approach 1:
The patent introduces an intermediary analysis layer that examines the relationship between input data characteristics and model output performance. This intermediary component analyzes whether changes in input data distribution or quality correlate with output anomalies, thereby recovering the causal relationship information that would otherwise be lost. This allows the system to maintain precise performance monitoring while also identifying whether input data issues or model degradation is the root cause
Solution Approach 2:
The system skips directly to root cause identification by implementing an efficient analysis mechanism that quickly determines whether input anomalies are causing output issues. Rather than merely detecting performance degradation, the system rushes through the diagnostic process to identify the causal link, thereby preventing the loss of critical causal relationship information while maintaining effective performance monitoring
Data Source
AI summary
In various embodiments of the present disclosure, output data generated by a deployed machine learning model may be received. An input data anomaly may be detected based at least in part on analyzing input data of the deployed machine learning model. An output data anomaly may further be detected based at least in part on analyzing the output data of the deployed machine learning model. A determination may be made that the input data anomaly contributed to the output data anomaly based at least in part on comparing the input data anomaly to the output data anomaly. A report may be generated that is indicative of the input data anomaly and the output data anomaly, and the report may be transmitted to a client device.