Data Skew Detection via Sliding Window Pattern Comparison
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Solution Overview
Problem
Machine learning models experience prediction performance degradation due to discrepancies between training and production data, leading to inaccurate predictions in high-stake environments like healthcare and finance, caused by differences in data distribution, transformation, and processing methods between the training and serving phases.
Innovation Solution
A system that detects and mitigates data skews by performing feature extraction, pattern analysis, and clustering on both training and production data, using a sliding window-based discrepancy detection method to compare reference and target patterns via vector-space quantitative distance techniques, ensuring upfront and runtime detection of skewness and improving model prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ML models are trained with data from training phase, then model learning and pattern recognition are achieved, but prediction performance degrades when deployed to production due to data distribution discrepancies
Solution Approach 1:
The system performs preliminary skew detection and mitigation by comparing training data patterns with production data patterns before deployment. A reference pattern is established from training data, and production data is analyzed against this reference to detect skews upfront, allowing corrective actions to be taken before the model encounters degraded performance in production.
Solution Approach 2:
The system implements a feedback mechanism where production data patterns are continuously monitored and compared against training data patterns. When skews are detected, the system generates notifications and alerts, enabling iterative refinement of the model or data preprocessing to maintain prediction accuracy over time.
2Manufacturing precision
If data transformation and processing are applied during training, then model performance is optimized, but discrepancies arise during serving phase when different processing is applied
Solution Approach 1:
The skew detection system serves multiple functions: it detects distribution shifts, identifies specific features causing skew, quantifies the degree of skew, and triggers alerts. This multi-functional approach addresses both performance optimization and processing consistency by providing a unified framework that works across different data transformation scenarios.
Solution Approach 2:
The system changes the parameter of data representation by extracting features and creating pattern representations (e.g., histograms, statistical summaries) that capture the essential characteristics of data distributions. This transformation allows for meaningful comparison between training and production data while being robust to different processing methods.
3Reliability
If batched data processing is used during training, then model convergence is achieved, but performance degrades when processing streamed data in smaller groups during serving
Solution Approach 1:
The system creates a copy of the training data distribution pattern (reference pattern) that can be used for comparison during production. This reference pattern captures the essential characteristics of batched training data, allowing the system to detect when streamed production data deviates from the expected distribution without requiring retraining.
Data Source
AI summary
Systems and methods for preventing prediction performance degradation by detecting and extracting skews in data during both training and production environments is described herein. Feature extraction may be performed on training data during the training phase, followed by pattern analysis that assesses similarities across labeled training data sets. A reference pattern may be derived from the pattern analysis and feature extraction of the training data. Feature extraction and pattern analysis may be performed on production data during the serving phase, and a target pattern may be derived from the pattern analysis and feature extraction of the production data. The reference pattern and target pattern may be fed to a discrepancy detection functionality to detect discrepancies by using a sliding window to move the target pattern across the reference pattern to make comparisons between the patterns. The comparison may provide a quantitative skew across the training and production data.