ML Retraining Architecture Selection for Data Drift Sustainability
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Solution Overview
Problem
Production machine learning (ML) systems experience performance degradation due to data drift, leading to decreased quality in model outputs over time, necessitating frequent retraining to maintain accuracy.
Innovation Solution
A retraining monitoring system that continuously collects contextual data to evaluate the sustainability of ML model platforms and architectures, using reinforcement learning and multi-armed bandit problems to optimize platform and architecture selection, ensuring minimal resource use and maintaining model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent retraining is performed to maintain model accuracy, then model performance is improved, but energy consumption and resource use increase
Solution Approach 1:
The system implements continuous monitoring of model performance metrics and data drift detection to provide feedback on when retraining is actually needed. This feedback mechanism prevents unnecessary retraining operations, allowing the system to maintain model accuracy while avoiding excessive energy consumption associated with frequent retraining cycles.
Solution Approach 2:
The retraining frequency and resource allocation are made dynamic rather than static. The system adapts retraining schedules based on actual performance degradation rates and data drift detection, adjusting computational resource usage accordingly. This dynamic approach ensures model accuracy is maintained while optimizing energy consumption by retraining only when necessary.
2Loss of energy
If multiple retraining platforms are used to optimize sustainability, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer (the monitoring and selection system) that manages multiple retraining platforms. This intermediary evaluates sustainability metrics and automatically selects the most energy-efficient platform for each retraining operation, thereby improving overall energy efficiency while shielding users from the underlying system complexity.
Solution Approach 2:
The system implements automated platform selection and sustainability evaluation that operates without manual intervention. The monitoring system self-manages the complexity of coordinating multiple retraining platforms by automatically assessing sustainability metrics and making optimal platform selections, reducing the burden on users while improving energy efficiency.
3Reliability
If continuous monitoring of contextual data is implemented, then sustainability evaluation is improved, but computational overhead increases
Solution Approach 1:
The system implements partial monitoring by focusing on the most critical sustainability metrics and contextual data points rather than continuously monitoring all possible parameters. This selective monitoring approach maintains reliable sustainability evaluation while reducing unnecessary computational overhead from tracking every possible variable.
Data Source
AI summary
A retraining monitoring system maintains the sustainability of a production machine learning (ML) model system that includes a production ML model retraining platform. The retraining monitoring system collects contextual data from the production ML model system and determines if one or more of a currently-selected architectural options has to be changed for sustainability. An architectural option of the production ML model retraining platform, such as, a processing location is selected from a cloud retraining platform or an on-premises retraining platform by a selection process based on a multi-armed bandit problem. An evaluation of the retraining architecture is dealt with as a reinforcement learning problem to implement one of a periodic retraining architecture or a reactive retraining architecture.