Data Quality Model for Drift-Resistant ML Inference Routing
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Solution Overview
Problem
Machine learning models used for data stream analysis are prone to accuracy degradation due to data quality issues such as mis-ordered sequences or incorrectly labeled events, which are difficult to detect and correct, especially in real-time or high-throughput applications.
Innovation Solution
A data quality model is employed to detect anomalous sequences, directing them to a less resource-intensive decision model for initial processing, and then synthesizing new sequences for training to update the models, ensuring accurate classification of future sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a more complex second decision model is used to process data streams, then classification accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system segments the decision-making process into two stages: first, a lightweight data quality model detects anomalous sequences; second, only sequences flagged as anomalous are processed by the complex second decision model. This segmentation allows the system to maintain high accuracy for critical cases while avoiding unnecessary computational overhead for normal sequences.
Solution Approach 2:
Instead of applying the complex second decision model to all sequences, the system applies it partially—only to sequences identified as anomalous by the data quality model. This partial action approach ensures that the computational resources are concentrated where they are most needed, maintaining accuracy for problematic cases while reducing overall resource consumption.
2Use of energy by moving object
If a less resource-intensive first decision model is used to process data streams, then computational resource consumption is reduced, but classification accuracy deteriorates
Solution Approach 1:
The system segments the processing pipeline into a first decision model for initial filtering and a second decision model for final classification. The first model handles the bulk of sequences with low resource consumption, while the second model provides accurate classification for the subset of anomalous sequences that require it.
Solution Approach 2:
The data quality model acts as an intermediary between the first decision model and the second decision model. It identifies anomalous sequences and directs them to the more accurate second model, thereby compensating for the limitations of the first model without requiring the first model to handle all sequences alone.
3Reliability
If data quality issues such as mis-ordered sequences or incorrectly labeled events are present, then model inference accuracy is damaged, but detecting these errors in the context of broader sequences is difficult
Solution Approach 1:
The system performs preliminary detection of anomalous sequences using the data quality model before they are processed by the decision models. By identifying potential errors in advance, the system can direct these sequences to the more accurate second decision model for re-evaluation, thereby correcting errors before they degrade overall inference accuracy.
Solution Approach 2:
The system implements a feedback mechanism where the data quality model continuously monitors sequences and identifies anomalies. When anomalous sequences are detected, they are fed back to the second decision model for re-processing, creating a closed-loop system that continuously improves accuracy by correcting errors through iterative refinement.
Data Source
AI summary
A method and related system for accounting for error drift in a machine learning model includes determining a first anomalous sequence in a first data stream by using a data quality model, providing the first data stream to a first decision model in lieu of a second decision model based on the first anomalous sequence, and determining a set of patterns based on the first anomalous sequence. The method further includes generating a set of synthetic sequences derived from the set of patterns, updating the data quality model based on the set of synthetic sequences.