Novel Data Detection for Self-Healing AI/ML Model Deployment
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Solution Overview
Problem
Conventional supervised learning paradigms for predictive models are limited by the correctness of ground truth data, leading to ineffective deployment in real-world contexts due to the novelty of production data differing from training data, especially in industrial applications where comprehensive real-world training data is scarce and costly to generate.
Innovation Solution
A combination of supervised and unsupervised machine learning models is used to detect and visualize novel data, employing feature segmentation and novelty classification to update prediction models based on test data observations, utilizing distance metrics and confusion matrices to refine model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If supervised learning models are trained using conventional methods with limited real-world training data, then model training cost and time are reduced, but model accuracy and reliability deteriorate when deployed to production environments with novel data distributions
Solution Approach 1:
The system implements continuous feedback loops where production data is systematically collected, analyzed for novelty using distance metrics and segmentation models, and used to trigger model retraining when novel patterns are detected. This feedback mechanism ensures the model adapts to real-world data distributions without requiring extensive manual retraining cycles
Solution Approach 2:
The system employs self-service mechanisms through automated novelty detection using unsupervised learning models (segmentation models and distance metrics) that automatically identify when production data diverges from training data distributions. This triggers autonomous model updating processes without requiring manual intervention, allowing the system to self-adapt to new data patterns
2Reliability
If extensive real-world training data is collected and used for model training, then model accuracy and adaptability to production scenarios are improved, but data collection cost, time, and complexity increase significantly
Solution Approach 1:
The system extracts only the essential information needed for model adaptation by using novelty detection to identify specific production data instances that differ from training distributions. Rather than collecting and processing all production data, it selectively extracts novel instances that warrant model updates, significantly reducing data processing complexity
Solution Approach 2:
The system performs preliminary action by pre-training segmentation models and establishing distance metrics before deployment. These pre-configured tools enable rapid novelty detection when production data arrives, eliminating the need for complex real-time data analysis and reducing on-the-fly processing complexity
Data Source
AI summary
In a training phase, training data may be used to train a supervised machine learning prediction model and an unsupervised machine learning segmentation model. Then, in a testing phase, the supervised machine learning prediction model may be used to predict a target outcome for a test data observation. Also, the unsupervised machine learning segmentation model may be used to evaluate the novelty of the test data observation relative to the training data.