Machine Learning Model Fine-Tuning for Operation Data Drift
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Solution Overview
Problem
Existing machine learning models face degradation in inference accuracy due to changes in the statistical trend of operation data over time, which are not adequately addressed by current automatic accuracy recovery techniques that fail to consider temporal data changes.
Innovation Solution
A machine learning device that performs density-based clustering on inference results, estimates labels for clusters, and performs fine-tuning on the model using operation data and estimated labels to adjust the model weights, thereby recovering inference accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic accuracy recovery is performed by retraining the machine learning model using operation data, then inference accuracy is improved, but the process requires significant computational resources and time
Solution Approach 1:
The patent performs preliminary actions by collecting operation data continuously in advance and preparing it for future retraining needs. The data collection mechanism operates proactively during normal model operation, accumulating labeled data that will be used when accuracy degradation is detected, thereby reducing the time required for actual retraining operations.
Solution Approach 2:
The patent implements dynamic adaptation by continuously monitoring model performance and adjusting the retraining process based on real-time conditions. When accuracy degradation is detected, the system dynamically initiates retraining using previously collected operation data, optimizing the timing and scope of computational resources based on actual model needs rather than following a fixed schedule.
2Adaptability or versatility
If the machine learning model is continuously trained with new operation data, then the model adapts to changing data trends, but the computational cost and resource consumption increase
Solution Approach 1:
The patent implements self-service by enabling the model to automatically monitor its own performance and trigger retraining processes without external intervention. The system self-evaluates accuracy metrics, detects degradation patterns, and initiates data collection and retraining operations autonomously, optimizing resource consumption by activating intensive computations only when actually needed rather than continuously.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting training parameters such as learning rate, batch size, and data selection criteria based on the current state of operation data and model performance. This allows the system to adapt the computational intensity and data processing parameters to match actual model needs, reducing unnecessary resource consumption while maintaining adaptability to changing trends.
3Reliability
If operation data is collected and processed for model retraining, then inference accuracy is recovered, but the complexity of the data processing pipeline increases
Solution Approach 1:
The patent segments the data processing pipeline into distinct modular components: data collection module, data preparation module, model evaluation module, and retraining module. Each component performs a specific function and can operate independently, reducing overall system complexity by breaking down the intricate retraining process into manageable, well-defined stages that are easier to implement and maintain.
Solution Approach 2:
The patent implements feedback mechanisms that automatically monitor model performance metrics and trigger data collection and retraining processes based on detected accuracy degradation. This closed-loop feedback system automates the coordination between different pipeline components, reducing operational complexity by using performance data to automatically orchestrate the entire retraining workflow without requiring complex manual coordination.
Data Source
AI summary
A process of collecting inference results that are obtained by inputting operation data to a trained machine learning model that has been trained based on training data, a process of generating clusters by performing density-based clustering on the collected inference results, a process of estimating, for each of the clusters, estimation labels associated with the corresponding clusters from among correct answer labels that correspond to all correct answers that can potentially be the inference results, and a process of performing fine-tuning on the trained machine learning model based on the pieces of operation data that belong to the corresponding clusters and based on the estimation labels associated with the corresponding clusters are performed.