Drift Pattern Calibration for AI Prediction Models
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Solution Overview
Problem
AI services face accuracy decreases due to data drift, requiring costly and labor-intensive relearning and redistribution processes, leading to delayed responses.
Innovation Solution
An apparatus and method for calibrating prediction models that detect latent factors in learning data to create drift patterns, prelearn calibration information, and determine similarity between input and recovery data, allowing for continuous calibration without replacing or relearning the prediction model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If relearning or redistribution is performed to restore service accuracy after data drift, then prediction accuracy is improved, but time consumption and operational costs increase
Solution Approach 1:
The system performs preliminary actions by creating multiple drift patterns in advance during the learning phase and pre-learning calibration information for each pattern. When data drift occurs in service, the system can immediately apply the appropriate pre-learned calibration information without performing time-consuming relearning or redistribution, thus restoring prediction accuracy quickly.
Solution Approach 2:
The system dynamically selects and applies calibration information corresponding to the detected drift pattern type. Instead of static relearning processes, the system adapts by choosing from pre-prepared calibration information based on the actual drift pattern observed, enabling rapid response to varying drift conditions.
2Reliability
If relearning or redistribution is performed to restore service accuracy after data drift, then prediction accuracy is improved, but operational costs increase
Solution Approach 1:
The system performs preliminary actions by creating multiple drift patterns in advance during the learning phase and pre-learning calibration information for each pattern. When data drift occurs in service, the system can immediately apply the appropriate pre-learned calibration information without performing time-consuming relearning or redistribution, thus restoring prediction accuracy quickly.
Solution Approach 2:
The system creates copies of calibration information for different drift patterns in advance. Instead of performing expensive relearning or redistribution operations when drift occurs, the system simply copies and applies the appropriate pre-learned calibration information, significantly reducing operational costs while maintaining prediction accuracy.
3Measurement precision
If manual labeling and verification processes are used to obtain new learning data, then data quality is improved, but labor requirements and processing time increase
Solution Approach 1:
The system performs self-service by automatically creating drift patterns and pre-learning calibration information without requiring manual labeling or expert inspection. The automated process generates the necessary calibration data and selects appropriate calibration information based on detected drift patterns, eliminating the need for manual data science tasks while maintaining data quality.
4Productivity
If relearning processes are automated to reduce manual intervention, then productivity is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by creating multiple drift patterns in advance during the learning phase and pre-learning calibration information for each pattern. When data drift occurs in service, the system can immediately apply the appropriate pre-learned calibration information without performing time-consuming relearning or redistribution, thus restoring prediction accuracy quickly.
Data Source
AI summary
An apparatus for calibrating prediction models of an inference service, including a computer program and a processor for executing the computer program according to an example embodiment of the present disclosure, wherein the apparatus includes: a drift pattern creating unit configured to detect a latent factor of learning data and create a possible drift pattern for the learning data based on the detected latent factor; and an instruction executing an individual drift calibrating unit configured to pre-learn calibration information according to a loss function between the learning data and the drift pattern for each drift pattern, and an ensemble drift calibrating unit including a similarity determining unit configured to perform prelearning to determine similarity between recovery data recovered by reconstructing the input drift pattern and the drift pattern.