Drift-Tolerant Machine Learning Model Selection
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Solution Overview
Problem
Machine learning models face inaccuracies and inefficiencies due to data drift, which occurs when real-world data changes over time, making it challenging for production models to maintain accuracy and efficiency across varying contexts.
Innovation Solution
The system generates divergent datasets simulating data drift by modifying relationships among attributes in the baseline dataset, allowing for the evaluation of machine learning models' performance in 'what if' scenarios and identifying the best models for deployment by ranking them based on accuracy, latency, and other metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained on historical data to make accurate predictions, then the model achieves high accuracy on training data, but the model becomes vulnerable to data drift when real-world data changes over time
Solution Approach 1:
The system performs preliminary actions by generating divergent datasets that simulate future data drift conditions before deploying the model to production. These divergent datasets are created by modifying the baseline training data to represent various drift scenarios (e.g., concept drift, covariate shift). The model is then evaluated on these pre-generated divergent datasets, allowing identification of drift-tolerant models before they face real-world data changes, thus preventing accuracy degradation rather than reacting to it after drift occurs.
2Adaptability or versatility
If a production machine learning model is designed to operate over a wide range of contexts, then the model increases versatility and adaptability, but the model becomes more susceptible to rapid drift at the edges of its intended scope
Solution Approach 1:
The system generates divergent datasets that specifically target edge cases and boundary conditions of the model's operational scope. By creating test scenarios that push the model to its limits under various drift conditions, the system identifies models that maintain accuracy at the edges of their intended scope before deployment. This preliminary evaluation ensures that versatile models selected for production have demonstrated robustness even in challenging boundary conditions.
Solution Approach 2:
The system implements feedback by evaluating model performance across multiple divergent datasets representing different drift scenarios and using this evaluation to rank and select models. The feedback loop identifies which models maintain accuracy under various drift conditions, allowing selection of models that are both versatile and reliable at operational boundaries.
3Productivity
If traditional machine learning evaluation methods are used that only assess performance on static training data, then the evaluation process remains simple and fast, but the evaluation fails to identify models that will perform well under data drift conditions
Solution Approach 1:
The system performs preliminary generation of divergent datasets that simulate various drift scenarios before model evaluation. This allows comprehensive evaluation of drift tolerance without requiring lengthy production deployments to assess model performance. The divergent datasets are generated by systematically modifying the baseline training data to represent different drift conditions, enabling thorough evaluation in advance.
Solution Approach 2:
The system creates synthetic copies of the training data with modified relationships among attributes to represent divergent scenarios. These copied and modified datasets serve as proxies for real-world drift conditions, allowing evaluation of model robustness without needing actual production data from future time periods. The copying approach maintains the structural characteristics of the original data while introducing controlled variations to simulate drift.
Data Source
AI summary
Techniques are disclosed for generating machine learning models that are insensitive to drift. A system trains a machine learning model using a divergent training dataset including synthesized data points simulating drift. The system can evaluate the machine learning models in terms of accuracy, latency, efficiency, and other metrics. Based on the evaluation, the system can select a machine learning model least susceptible to drift.