Post-Deployment Learning Model Optimization via Dynamic Triggering
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Solution Overview
Problem
Machine learning and artificial intelligence algorithms deployed in production environments often require re-optimization due to changing conditions, but existing methods lack efficient mechanisms for post-deployment model re-adjustment and performance monitoring.
Innovation Solution
A method and system for optimizing learning models post-deployment by detecting model re-adjustment triggers, selecting and deploying learning models with adjusted states, and monitoring performance to dynamically replace models based on determination of superior performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are deployed in production environments, then they can provide predictive capabilities and value to users, but they become difficult to re-optimize and adapt to changing conditions
Solution Approach 1:
The patent implements dynamic model management by enabling models to be re-trained and re-deployed in production environments. The system allows model states to be adjusted, re-trained with new data, and swapped dynamically based on performance triggers, transforming static deployed models into adaptable, evolving systems that can respond to changing conditions without requiring complete re-deployment cycles.
Solution Approach 2:
The patent segments the model lifecycle into distinct, manageable components: model training, model state adjustment, model deployment, and model monitoring. By separating these functions and enabling independent manipulation of model states, the system reduces the complexity of re-optimization by allowing targeted updates to specific model aspects without affecting the entire model pipeline.
2Measurement precision
If models are re-trained frequently to improve performance, then model accuracy improves, but computational resources and time are consumed
Solution Approach 1:
The patent implements partial re-training by allowing selective adjustment of model states using only the portions of data or model components that need updating. Instead of always performing complete model re-training, the system enables targeted re-optimization of specific model aspects, reducing the time and computational resources required while still improving accuracy when needed.
Solution Approach 2:
The patent performs preliminary model adjustments and validations before full re-deployment. By pre-processing data, pre-training model updates, and validating improvements in advance, the system reduces the actual re-training time required in production and ensures that re-training only occurs when beneficial, thereby minimizing time loss while maintaining accuracy improvements.
3Reliability
If model performance is monitored continuously, then model degradation can be detected early, but system complexity and computational overhead increase
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor model performance metrics in production environments. The system tracks performance degradation, compares it against predefined thresholds, and automatically triggers re-training or model swapping when degradation is detected. This feedback loop enables early detection of model issues while maintaining manageable complexity through automated decision-making and threshold-based triggers.
4Productivity
If multiple model versions are maintained for A/B testing, then model selection can be optimized, but storage and deployment complexity increase
Solution Approach 1:
The patent creates and manages copies of model versions for A/B testing and comparison purposes. Instead of managing complex model variants, the system generates copies of base models with different configurations or trained on different data sets, allowing systematic comparison and selection. This copying approach simplifies model management by treating different versions as replicable units that can be deployed, compared, and swapped without increasing fundamental system complexity.
Data Source
AI summary
A method and system for optimizing a learning model post-deployment. Specifically, the disclosed method and system re-optimize—i.e., re-train and/or re-validate—machine learning and/or artificial intelligence algorithms that have already been deployed into a production environment. During post-deployment, the re-optimization process may transpire following the advent of varying model re-adjustment triggers.