ML Model Anomaly Detection for Business Output Impact Analysis
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Solution Overview
Problem
Evaluating the accuracy and performance of numerous machine learning models deployed in enterprises is time-consuming and daunting due to their large number, making it difficult to detect anomalies and their impact on business output data effectively.
Innovation Solution
A real-time server system and method for determining anomalies in machine learning models by performing statistical analysis on machine learning model variables within time windows, using algorithms like Isolation Forest and XGBoost, to identify deviations and their impact on business decisions, and providing real-time remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional evaluation methods are used for machine learning models, then evaluation thoroughness is improved, but evaluation time and resource consumption increase significantly
Solution Approach 1:
The patent segments the evaluation process into distinct components: anomaly detection module that identifies deviations in model behavior, impact analysis module that assesses business consequences, and root cause analysis module that traces anomalies to specific model changes. This segmentation allows parallel processing of different evaluation aspects, reducing total evaluation time while maintaining comprehensive coverage
Solution Approach 2:
The system performs preliminary actions by continuously monitoring model behavior in production and establishing baseline performance metrics before full evaluation. The anomaly detection component proactively identifies deviations from baselines, allowing the system to focus detailed analysis only on models showing anomalies, thus reducing overall evaluation time while maintaining thoroughness
2Measurement precision
If comprehensive anomaly detection is performed across all machine learning models, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal anomaly detection framework that can evaluate multiple types of machine learning models (classification, regression, recommendation systems) using the same core detection algorithms. The system performs multiple functions including anomaly detection, impact analysis, and root cause identification within a single integrated platform, reducing the need for separate specialized systems for each model type
Solution Approach 2:
The system introduces intermediary components that simplify the complexity of comprehensive anomaly detection. The baseline comparison module acts as an intermediary, comparing current model behavior against historical baselines to filter out normal variations before applying complex anomaly detection algorithms. This intermediary layer reduces the computational burden and simplifies the overall detection process
3Speed
If real-time monitoring of machine learning models is implemented, then anomaly detection speed is improved, but computational resource consumption increases
Solution Approach 1:
The system applies partial action by performing lightweight anomaly detection continuously in real-time, and only triggering full detailed analysis when anomalies are detected. The monitoring layer uses minimal computational resources to track key metrics, while the more resource-intensive analysis components are activated selectively based on anomaly thresholds, balancing speed and resource consumption
Solution Approach 2:
The patent merges multiple evaluation functions into a unified real-time monitoring system. The anomaly detection, impact analysis, and root cause identification modules are combined and executed as an integrated pipeline, allowing the system to achieve comprehensive evaluation at real-time speeds by sharing computational resources and data structures across all functions rather than running separate independent analyses
Data Source
AI summary
In one example, a non-transitory computer-readable storage medium with program instructions for determining anomalies in machine learning models and impact on business output data thereof is disclosed. The method accesses machine learning model variables for a machine learning model, and determines a deviation in behavior of the machine learning model by performing statistical analysis on each machine learning model variable to identify statistical differences between machine learning model variable data captured in a given time window and data captured in a previous time window at the same time of a prior day, prior week, etc. If statistical differences exist, the method performs anomaly detection to determine whether the statistical differences are an anomaly. If so, the method determines whether the anomaly causes an impact on business decision output data, and if so, identifying a root cause and generating a notification alert for corrective action.