Novel Data Detection in Self-Healing AI Models
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Solution Overview
Problem
Conventional machine learning predictive models face limitations in predicting machine failures due to the novelty of production data compared to training data, especially in real-world contexts where diverse conditions and limited training data availability hinder the creation of effective pre-trained models.
Innovation Solution
The integration of supervised and unsupervised machine learning approaches, where a pre-trained supervised prediction model is deployed with an unsupervised segmentation model to classify novel data, allowing for incremental updates based on new observations and facilitating the detection and visualization of novel data through distance metrics and user interfaces, enabling model updates and improved predictive performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a pre-trained supervised prediction model is deployed in production environment, then predictive performance is improved, but the model becomes constrained by the novelty of production data with respect to training data distribution
Solution Approach 1:
The system implements feedback by continuously monitoring distance metrics between test data observations and feature segments, using this information to detect novel data and trigger model updates. The feedback loop enables the model to adapt to production data distribution shifts while maintaining predictive performance through incremental retraining with novel observations.
Solution Approach 2:
The feature segmentation model is pre-trained on training data to create feature segments before deployment. This preliminary action establishes a reference framework that enables rapid detection of novel data in production without requiring full model retraining, allowing the system to prepare for adaptability challenges in advance.
2Measurement precision
If extensive real-world training data is collected to improve model accuracy, then predictive accuracy is improved, but data availability and deployment time are reduced
Solution Approach 1:
Instead of requiring complete retraining with extensive new data, the system applies partial updates by incorporating only novel data observations into model retraining. This partial action approach achieves accuracy improvement without the time cost of collecting and processing extensive training datasets, updating models incrementally with only the necessary new information.
Solution Approach 2:
The system segments training data into feature segments based on similarity, allowing efficient comparison of new observations against representative segments rather than entire training datasets. This segmentation enables rapid novelty detection and reduces the computational burden of model updates, decreasing deployment time while maintaining accuracy.
3Measurement precision
If distance metrics are calculated for all training data observations to detect novel data, then detection precision is improved, but computational complexity increases
Solution Approach 1:
The training data is segmented into representative feature segments, and distance metrics are calculated only against these segments rather than all individual training observations. This segmentation maintains novelty detection precision by capturing essential data patterns while reducing computational complexity through dimensionality reduction.
Solution Approach 2:
Instead of comparing against all original training data, the system uses copied representative feature segments that capture the essential characteristics of training data distributions. These segment copies enable efficient distance metric calculation while preserving detection precision through representative sampling of the full data space.
Data Source
AI summary
A feature data segment may be determined by applying a feature segmentation model to a test data observation. The feature segmentation model may be pre-trained via a plurality of training data observations and may divide the plurality of training data observations into a plurality of feature data segments. A predicted target value may be determined by applying to a test data observation a prediction model pre-trained via a plurality of training data observations. One or more distance metrics representing a respective distance between the test data observation and the feature data segment along one or more dimensions may be determined. The one or more distance metrics may be represented in a user interface. An updated prediction model and an updated feature segmentation model that both incorporate the test data observation and the training data observations may be determined based on user input.