ML Model Retraining via Production Data Characteristic Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models often fail to accurately predict production data due to deficiencies in training datasets that do not adequately represent the characteristics of the production data, especially in data-sensitive environments where access to production data is restricted, leading to inefficient retraining processes.
Innovation Solution
A controller system that identifies statistically significant characteristics of production data underrepresented in the training dataset, providing these characteristics without exposing sensitive production data, to update and retrain the model, ensuring improved accuracy by generating a supplemental training dataset that reflects the production environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the training dataset is used to train the machine learning model, then the model can be deployed in production, but the model accuracy deteriorates when production data characteristics are not represented in the training data
Solution Approach 1:
The system continuously monitors model performance on production data and uses this feedback to identify underrepresented characteristics. The feedback loop includes: collecting production data characteristics, comparing them against training data representation, identifying gaps, and using this information to augment the training dataset. This iterative feedback process ensures the training data progressively better represents production conditions, improving model accuracy over time.
Solution Approach 2:
Before deploying the model to production, the system performs preliminary analysis of production data characteristics and proactively identifies potential representation gaps in the training data. By conducting this analysis in advance and augmenting the training dataset beforehand, the system prevents accuracy deterioration from occurring in the first place, rather than reacting after problems arise.
2Manufacturing precision
If production data is accessed to identify training data deficiencies, then the training data quality can be improved, but data sensitivity and security risks increase
Solution Approach 1:
The system extracts only the necessary characteristics from production data for analysis purposes, separating the high-value information needed for training data augmentation from the sensitive data itself. By taking out only the characteristic metadata (such as data types, distributions, and patterns) while leaving the actual sensitive production records inaccessible, the system enables training quality improvement without compromising data security.
Solution Approach 2:
The system introduces an intermediary layer between the production data and the training data augmentation process. This intermediary component analyzes production data characteristics in a controlled environment, identifies representation gaps, and generates augmented training data without direct access to or exposure of the sensitive production data. The intermediary acts as a buffer that protects sensitive information while still enabling quality improvement.
3Reliability
If the training dataset is augmented with production data characteristics, then model accuracy improves, but the complexity of the retraining process increases
Solution Approach 1:
The retraining process is segmented into distinct, manageable phases: (1) collecting production data characteristics, (2) analyzing model performance on production data, (3) identifying underrepresented characteristics, (4) augmenting the training dataset with synthesized data representing these characteristics, and (5) retraining the model. By breaking down the complex retraining process into these sequential segments, the system reduces overall complexity and makes each step more manageable and automated.
Data Source
AI summary
A machine-learning model that is using production data and is operating in a production environment within a data-sensitive realm is analyzed, where this model was trained using a training dataset. An accuracy of the model is identified as falling below an accuracy threshold when providing one or more predictions of a subset of the production data. At least one characteristic of the production data that is used to predict the subset of the production data is determined to be underrepresented in the training dataset. The one or more predictions and the at least one characteristic are provided to a location outside of the production environment.