Anomaly Back-Testing Service for ML Model Validation
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Solution Overview
Problem
Machine learning techniques for anomaly detection require extensive expertise, making them difficult for users to utilize effectively, and there is a need to ensure the accuracy and reliability of predictions without revealing underlying technical details.
Innovation Solution
An anomaly detection service with integrated back-testing capabilities that allows users to evaluate the efficacy of machine learning models by training and testing them on historical data, providing confidence in their performance for real-time operations, and enabling continuous anomaly detection with adaptable workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are used for anomaly detection, then prediction accuracy is improved, but ease of operation deteriorates due to extensive expertise required
Solution Approach 1:
The patent introduces an intermediary evaluation system that mediates between the complex machine learning models and the end user. This system automatically evaluates model performance using back-testing on historical data and presents results in an accessible format, shielding users from technical complexities while maintaining high prediction accuracy through sophisticated ML techniques
2Reliability
If back-testing is performed to evaluate machine learning models, then reliability is improved, but loss of time increases due to extensive testing requirements
Solution Approach 1:
The patent applies preliminary action by performing back-testing evaluations before deploying machine learning models to production. Historical data is used to pre-evaluate model performance under various scenarios, ensuring reliability is established in advance. This allows organizations to assess model robustness beforehand, reducing the risk of failures during actual operations
Solution Approach 2:
The system enables continuous back-testing and evaluation workflows that can operate automatically and continuously. By establishing ongoing evaluation processes rather than one-time assessments, the system maintains continuous improvement of model reliability while optimizing the time investment through automated, efficient testing procedures
Data Source
AI summary
A service to provide anomaly detection receives a request to back-test the service. The request includes information for accessing a dataset of historical data. The service executes workflows to ingest the data, train a plurality of machine learning models to perform anomaly detection, and detect anomalies in the dataset. A representation of the detect anomalies is generated and presented to a user. The service receives an indication to activate the service to provide ongoing anomaly detection services.