ML Model Monitoring via Distance-Based Sampling
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Solution Overview
Problem
Machine learning models deployed in production environments often produce less accurate predictions due to differences in data types between training and production data, leading to incorrect workflow actions in downstream systems.
Innovation Solution
A system that monitors the execution of machine learning models by initializing a review dataset based on training data, sampling a subset of elements from production data that maximizes distance from the review dataset, and presenting these samples to users for feedback to evaluate and potentially retrain the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained using training data in development environment and deployed to production environment, then the model can be deployed and used for making predictions, but the predictions may be less accurate due to differences in data types between training and production environments
Solution Approach 1:
The system performs preliminary actions by initializing a review dataset based on training data before deployment, and by sampling production data to identify discrepancies. This proactive approach allows the system to detect and address data distribution differences before they significantly impact prediction accuracy, thereby resolving the contradiction between reliability and adaptability
Solution Approach 2:
The system implements feedback mechanisms by comparing production data samples with training data, evaluating model performance on these samples, and using the results to determine whether retraining is needed. This closed-loop feedback system enables continuous improvement of prediction accuracy while adapting to production data characteristics
2Measurement precision
If all production data samples are reviewed by users for feedback, then the evaluation of machine learning model accuracy is comprehensive, but the time and resources required for review increase significantly
Solution Approach 1:
The system extracts and focuses on the most critical subset of production data for user review by sampling elements that maximize distance from the review dataset initialization. This selective extraction approach maintains comprehensive evaluation accuracy while significantly reducing the time and resources required compared to reviewing all production data samples
Solution Approach 2:
The system applies partial action by reviewing only a strategically selected subset of production data rather than all samples. The sampling mechanism ensures that this partial review captures the essential variations and potential issues in the model's performance, achieving sufficient evaluation accuracy without the excessive time cost of complete data review
Data Source
AI summary
A system monitors execution of a machine learning model in an environment, for example, development environment or production environment. The system receives a training dataset and a production dataset. The system initializes a review dataset based on elements of the training dataset. The system samples a subset of elements of the production dataset by identifying elements from the production dataset based on their distance from elements of the review dataset. The system sends elements of the review dataset for presentation via a user interface for receiving user feedback indicating accuracy of the result of execution of the machine learning model. The execution of the machine learning model is monitored to make determination regarding deployment of the model in a production environment for continuous delivery of the model or for evaluation or quality assurance of model executing in an environment.