Machine Learning Model Management for Noisy Inspection Data
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Solution Overview
Problem
Existing manufacturing systems face challenges in defect detection due to false positives and negatives in machine learning models, particularly due to environmental variations and unaccounted factors like lighting and focus, leading to waste and inefficiency.
Innovation Solution
A model management system that refines training datasets by identifying and addressing environmental factors causing noisy data points, retraining the model with a refined dataset to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manufacturers increase false positives to reduce defective product distribution, then defect detection reliability improves, but product waste increases
Solution Approach 1:
The system changes the parameters of the training dataset by identifying and removing noisy data points that cause false positives. By modifying the data distribution through selective removal of problematic training examples, the model's decision boundary shifts to reduce false positives while maintaining defect detection reliability.
Solution Approach 2:
The system implements feedback by continuously monitoring model predictions and identifying false positives. These false positives are fed back into the training process as noisy data points to be removed, creating an iterative refinement loop that progressively improves model accuracy and reduces waste.
2Ease of manufacture
If manufacturers use traditional model training with fixed training datasets, then model development simplicity is maintained, but model performance under environmental variations deteriorates
Solution Approach 1:
The system enables self-service by automatically identifying noisy data points and removing them without requiring manual intervention. The model itself generates the feedback needed for improvement through its predictions, and the system autonomously refines the training dataset and retrain the model, maintaining simplicity while improving performance.
Solution Approach 2:
The system introduces dynamics by making the training dataset mutable and adaptable. Instead of using a fixed training set, the system continuously identifies and removes noisy data points, dynamically adjusting the training distribution to better represent actual operating conditions across environmental variations.
3Productivity
If manufacturers deploy models without refining training data, then deployment speed is maintained, but prediction accuracy for noisy data points deteriorates
Solution Approach 1:
The system performs preliminary action by identifying and removing noisy data points from the training dataset before final model deployment. This preprocessing step prepares the model in advance to handle real-world variations, improving prediction accuracy without significantly delaying deployment through the automated nature of the refinement process.
Data Source
AI summary
A model management system adaptively refines a training dataset for more effective visual inspection. The system trains a machine learning model using the initial training dataset and sends the trained model to a client for deployment. The deployment process generates outputs that are sent back to the system. The system determines that performance of predictions for noisy data points are inadequate and determines a cause of failure based on a mapping of the noisy data point to a distribution generated for the training dataset across multiple dimensions. The system determines a cause of failure based on an attribute of the noisy datapoint that deviates from the distribution of the training dataset and performs refinement towards the training dataset based on the identified cause of failure. The system retrains the machine learning model with the refined training dataset and sends the retrained machine learning model back to the client for re-deployment.