Drift Forecasting for Edge Model Selection
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Solution Overview
Problem
Machine learning models deployed in production environments face performance drift, making them unreliable, and existing drift detection methods often require constant monitoring and retraining, which can be resource-intensive and time-consuming.
Innovation Solution
The system comprises an offline stage that determines a minimal set of alternative models and their performance parameters, and an online stage that deploys a shadow model to replace the drifting model, allowing for a fast hand-off to a better-performing alternative model without the need for retraining, leveraging baseline performance evaluations and interpolation to select the most suitable model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If performance-based drift detection is used to monitor model quality, then model reliability is maintained, but resource consumption and time cost increase due to constant monitoring and retraining
Solution Approach 1:
The system pre-computes drift forecasts and identifies alternative models before actual drift occurs. By performing drift detection and alternative model identification in advance, the system avoids the need for constant monitoring and urgent retraining, thereby reducing resource consumption while maintaining model reliability.
Solution Approach 2:
The system uses the drifting model's own performance evaluations and drift patterns to automatically identify suitable alternative models. This self-service mechanism eliminates the need for external constant monitoring and manual retraining interventions, reducing resource overhead while ensuring model reliability.
2Reliability
If constant monitoring and retraining are performed to maintain model performance, then model accuracy is preserved, but time consumption increases
Solution Approach 1:
The system performs drift forecasting and alternative model identification before actual drift impacts model accuracy. By anticipating drift and preparing alternative models in advance, the system minimizes the time needed to respond to drift events while maintaining model accuracy.
Solution Approach 2:
The system continuously monitors model performance evaluations and uses this feedback to detect drift patterns early. This feedback mechanism enables timely identification of drift and automatic selection of alternative models, reducing the time lag between drift occurrence and model replacement while preserving accuracy.
3Speed
If alternative models are kept ready for quick replacement, then response speed to drift increases, but system complexity increases
Solution Approach 1:
The system maintains a pool of alternative models that can serve multiple purposes: they can be used for drift detection, as backup models, or as primary models when drift is detected. This multi-functionality reduces the need for dedicated drift detection mechanisms and simplifies the overall system architecture while enabling fast response to drift.
Solution Approach 2:
The system introduces a drift forecast module that acts as an intermediary between model monitoring and model replacement. This intermediary analyzes model performance evaluations and drift patterns to automatically select appropriate alternative models, simplifying the decision-making process and reducing system complexity while maintaining fast response speed.
4Reliability
If retraining is performed to adapt models to drifted data, then model performance is restored, but productivity decreases due to resource-intensive processes
Solution Approach 1:
Instead of retraining expensive and time-consuming models, the system uses pre-trained alternative models that are already adapted to different data distributions. These alternative models serve as disposable replacements that can be quickly deployed without retraining, restoring model performance while maintaining productivity.
Solution Approach 2:
The system changes the parameter being optimized from model training to model selection. Instead of modifying model parameters through retraining, the system selects from pre-trained models with different parameter configurations that are already suited for drifted data distributions, thereby restoring performance without the productivity loss associated with retraining.
Data Source
AI summary
One example method includes obtaining, by a central node, an evaluation of a performance of a reference model deployed at an edge node, determining if the evaluation exceeds a threshold associated with the reference model, and incrementing a counter when the evaluation exceeds the threshold, when a counter value equals or exceeds a specified limit, performing an interpolation process to identify a new model having better expected performance than performance of the reference model, and deploying the new model in a shadow mode at the edge node.