Futureproofing Machine Learning Models via Evolutionary Algorithms
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Solution Overview
Problem
Machine learning models deteriorate quickly after deployment due to covariate shift, prior probability shift, and concept drift, requiring frequent monitoring and retraining, which is resource-intensive and inefficient.
Innovation Solution
Futureproofing machine learning models by generating a futureproofed version using historical data and evolutionary algorithms, incorporating rules that adapt over time to predict and mitigate drifts, thereby extending the model's lifecycle without retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are deployed in production environments, then they can provide predictive outcomes from data, but they deteriorate quickly due to covariate shift, prior probability shift, and concept drift
Solution Approach 1:
The patent applies preliminary action by generating a futureproofed model before deployment that anticipates and adapts to future data distributions. The model is trained on historical data representing future conditions, preparing it in advance to handle covariate shift, prior probability shift, and concept drift without requiring immediate retraining when deterioration occurs.
Solution Approach 2:
The patent implements dynamics by creating a model that incorporates temporal adaptability. The futureproofed model uses historical data from different time periods to learn evolving patterns, enabling it to dynamically adjust to changing data distributions over time rather than remaining static.
2Reliability
If machine learning models are frequently monitored and retrained to maintain performance, then model reliability is improved, but resource consumption and operational complexity increase
Solution Approach 1:
The patent eliminates the need for frequent monitoring and retraining by performing the adaptation action in advance. The futureproofed model is generated once using historical data before deployment, preventing performance deterioration rather than correcting it after occurrence, thus removing the need for continuous monitoring infrastructure.
Solution Approach 2:
The patent enables the model to serve itself by incorporating historical adaptability during the training phase. The futureproofed model inherently possesses the capability to handle data distribution shifts without requiring external monitoring systems or manual retraining interventions, making the system self-sufficient.
3Measurement precision
If machine learning models are retrained frequently to adapt to data drift, then model accuracy is maintained, but time and computational resources are wasted
Solution Approach 1:
The patent performs the adaptation action in advance by training the model on historical data that represents future conditions before deployment. This preliminary adaptation ensures the model is prepared for upcoming data distributions, eliminating the need for time-consuming retraining cycles after deployment.
Solution Approach 2:
The patent changes the training parameters by incorporating historical data from multiple time periods into the model training process. This parameter change allows the model to learn temporal patterns and adapt to evolving data distributions, maintaining accuracy without requiring frequent retraining.
4Adaptability or versatility
If historical data is used to generate futureproofed models, then model adaptability to future conditions is improved, but the complexity of model generation operations increases
Solution Approach 1:
The patent enhances adaptability by changing the training parameters to include historical data from different time periods. This parameter modification allows the model to learn temporal patterns and adapt to future data distributions while using standard machine learning training procedures.
Solution Approach 2:
The patent achieves versatility by creating a single futureproofed model that can handle multiple types of data drift (covariate shift, prior probability shift, concept drift) simultaneously. The model generation process, while enhanced, produces a universal solution that addresses various deterioration mechanisms without requiring separate models for each type of drift.
Data Source
AI summary
Provided are a computer-implemented method, a system, and a computer program product for futureproofing a machine learning model, in which historical data for updates and changes to a baseline machine learning model are received. A futureproofing metric is generated. An enhanced machine learning model comprising a futureproofed version of the baseline machine learning model is generated with the historical data and the baseline machine learning model as inputs.