Automatic Offline Machine Learning Model Retraining
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Solution Overview
Problem
Machine learning models, such as those used in retail and online streaming services, experience performance degradation over time due to changes in user behavior, making it difficult to monitor and update their effectiveness in real-time, leading to suboptimal recommendations and predictions.
Innovation Solution
The method involves creating retraining data by combining execution logs from prediction services with observed user actions, allowing for the automatic updating of machine learning models in real-time or near real-time, enabling the learning of retrained models that can improve performance by updating weight values based on differences between predicted and actual user actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an offline machine learning model is trained with historical data, then the model can generate predictions and recommendations, but the model performance degrades over time due to changes in user behavior
Solution Approach 1:
The system implements feedback by continuously monitoring actual user actions against model predictions and using this information to automatically retrain the model. Execution logs containing predicted actions and observed user actions are fed back into the retraining process, allowing the model to learn from its own performance and adapt to changing user behavior patterns over time.
Solution Approach 2:
The patent enables continuous model improvement by establishing an ongoing retraining process that operates continuously or periodically. Rather than training the model once and then using it indefinitely, the system maintains continuous learning from new user data, ensuring the model remains current and effective throughout its deployment lifecycle.
2Reliability
If the model is retrained frequently to maintain performance, then model accuracy improves, but the complexity of the system increases
Solution Approach 1:
The system performs self-service retraining by automatically generating retraining data from execution logs and observed user actions without requiring manual intervention. The retraining process is autonomous, with the system monitoring its own performance and initiating retraining cycles based on predefined criteria, thereby reducing operational complexity while maintaining high accuracy.
Solution Approach 2:
The patent implements preliminary action by pre-establishing retraining criteria and data collection mechanisms before performance degradation occurs. Execution logs are continuously collected and structured in advance, and retraining triggers are pre-configured, allowing the model to be updated proactively rather than reactively, which simplifies the overall system architecture.
3Adaptability or versatility
If real-time model updates are implemented, then recommendations remain relevant, but the difficulty of monitoring and detecting performance changes increases
Solution Approach 1:
The system uses feedback mechanisms to monitor model performance by comparing predicted actions against actual user actions recorded in execution logs. This continuous feedback loop automatically detects performance degradation and triggers retraining when necessary, making performance monitoring transparent and manageable while maintaining high adaptability to user behavior changes.
Data Source
AI summary
Systems and methods are provided for retraining a machine learning model to learn a retrained machine learning model. After a computing system receives an execution log of a prediction service and an observed user action, it may create retraining data. From the retraining data, the computing system may learn a retrained machine learning model. The retrained machine learning model can be used to compute future predictions of user actions if it has an improved performance over the original machine learning model.