Active Surveillance for Machine Learning Model Adaptation
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Solution Overview
Problem
Existing machine learning (ML) models face challenges in maintaining performance when deployed in real-world environments due to limitations in the scope of their training data, leading to inefficiencies and inaccuracies as they encounter new data types and variations not covered in the initial training dataset.
Innovation Solution
The integration of active surveillance and learning techniques, which include model scope evaluation and domain adaptation, to monitor and update ML models in real-time, using automated processes to assess performance and adapt to new data, thereby improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a primary ML model is trained on a limited training dataset, then the model can be deployed quickly with initial performance, but the model performance deteriorates when encountering new data types and variations not covered in the training dataset
Solution Approach 1:
The patent implements dynamic model adaptation by continuously monitoring model performance on incoming data and automatically triggering retraining processes when performance degradation is detected. The system transitions from a static trained model to a dynamic system that adapts its structure and parameters based on real-world data distributions, resolving the contradiction between quick deployment and sustained performance.
Solution Approach 2:
The patent establishes a feedback loop where model performance is continuously evaluated on production data, and this performance information feeds back into the training process. When performance metrics fall below thresholds, the system automatically initiates retraining with new data, creating a closed-loop system that maintains reliability without sacrificing deployment speed.
2Reliability
If manual monitoring and updating of ML models is performed, then model performance can be maintained, but the process requires significant human intervention and time
Solution Approach 1:
The patent implements self-service automation where the ML model monitoring and updating system performs tasks autonomously without human intervention. The system automatically detects performance degradation, collects new data, retrains models, and deploys updated versions, eliminating the need for manual monitoring and updating while maintaining high model performance.
Solution Approach 2:
The patent performs preliminary actions by pre-configuring performance thresholds, automated retraining pipelines, and deployment workflows before production use. This preparation enables the system to respond automatically to performance issues without requiring human decision-making, significantly reducing manual intervention time while maintaining reliability.
3Measurement precision
If the training dataset scope is expanded to cover more data variations, then model accuracy improves on diverse data, but the complexity of data collection and model training increases
Solution Approach 1:
The patent applies partial action by focusing data collection and model retraining only on the specific data variations that cause performance degradation, rather than attempting to pre-cover all possible variations. The system collects and processes only the necessary subset of new data types encountered in production, reducing complexity while maintaining accuracy on relevant data.
Solution Approach 2:
The patent performs preliminary data processing and validation steps before model retraining, establishing standardized pipelines that automate data collection, cleaning, and preparation. This preliminary structuring of data workflows reduces the complexity of handling expanded training datasets by creating reusable, automated processes.
4Measurement precision
If automated active surveillance techniques are implemented to monitor model performance, then model accuracy can be maintained on new data, but the system complexity increases
Solution Approach 1:
The patent implements multi-functional surveillance components that perform multiple tasks simultaneously. The performance monitoring system not only tracks accuracy metrics but also detects data distribution shifts, triggers retraining workflows, and validates model updates, reducing overall system complexity by consolidating functions into unified components.
Solution Approach 2:
The patent employs feedback mechanisms where surveillance system outputs directly control subsequent actions. Performance metrics feed back into automated decision-making logic that triggers or prevents retraining, creating a self-regulating system that maintains accuracy while managing complexity through rule-based automation rather than complex manual processes.
Data Source
AI summary
Techniques are described for performing active surveillance and learning for machine learning (ML) model authoring and deployment workflows. In an embodiment, a method comprises applying, by a system comprising a processor, a primary ML model trained on a training dataset to data samples excluded from the training dataset to generate inferences based on the data samples. The method further comprises employing, by the system, one or more active surveillance techniques to regulate performance of the primary ML model in association with the applying, wherein the one or more active surveillance techniques comprise at least one of, performing a model scope evaluation of the primary ML model relative to the data samples or using a domain adapted version of the primary ML model to generate the inferences.