Predictive Model Intermediate Operation Drift Detection
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Solution Overview
Problem
Predictive models often experience data drift over time, leading to decreased accuracy in predictions, making it difficult to identify and adjust parameters in a timely manner, resulting in reduced performance and efficiency.
Innovation Solution
A computer system identifies and monitors intermediate operations within predictive models, generating and comparing output values from training and real-world data to detect data drift, sending notifications when differences exceed a threshold, allowing for timely adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive models are monitored using only final output values, then the monitoring process remains simple, but data drift cannot be detected early enough to maintain prediction accuracy
Solution Approach 1:
The patent segments the predictive model into multiple intermediate operations and monitors the output values of these intermediate operations separately. This allows detection of data drift at earlier stages before it affects the final prediction, improving detection timing while maintaining manageable complexity through modular monitoring of individual operation outputs
2Reliability
If intermediate operations are monitored to detect data drift earlier, then prediction accuracy can be maintained, but the complexity of the monitoring system increases
Solution Approach 1:
The patent implements preliminary monitoring of intermediate operation outputs during model execution. By capturing and comparing output values from intermediate operations as they are generated, the system can detect data drift before it propagates to the final prediction, maintaining reliability while using a systematic approach to manage complexity
3Reliability
If the model parameters are adjusted frequently to maintain accuracy, then prediction performance improves, but the loss of time for retraining and adjustment increases
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors intermediate operation outputs and compares them against expected ranges. When drift is detected, the system provides feedback signals that trigger targeted parameter adjustments only for affected model components, improving performance while reducing overall adjustment time through selective rather than comprehensive retraining
Data Source
AI summary
A model including a plurality of intermediate operations may be identified. An intermediate operation of the plurality of intermediate operations may be selected to provide output values associated with the plurality of intermediate operations during execution of the model. A first output value associated with the intermediate operation may be received during a first execution of the model. A second output value associated with the intermediate operation may be received during a second execution of the model. A difference between the first output value and the second output value may be determined to satisfy a threshold. A notification may be transmitted that indicates data drift of the model in view of the difference between the first output value and the second output value satisfying the threshold.