ML Model Governance Using Placebo Data Validation
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Solution Overview
Problem
ML models deployed in production environments face unpredictability as they learn from real-time operational data, leading to potential degradation and adverse outputs due to inputs that should have no impact, jeopardizing business continuity.
Innovation Solution
Implement placebo data injection validation to train and validate ML models using data that should not affect outputs, enabling detection of model degradation and proactive adjustments to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ML models are trained on real-time operational data to improve adaptability, then the model can learn from experience and improve performance, but the model may suffer from unpredictable degradation and produce adverse outputs due to inputs that should have no impact
Solution Approach 1:
The system performs preliminary validation by training a copy of the ML model on placebo data (data that should have no impact) before deployment. This advance preparation allows detection of potential degradation issues before they affect production models, resolving the contradiction by preparing protective measures in advance while maintaining model adaptability through normal training.
Solution Approach 2:
The system introduces an intermediary validation mechanism that compares outputs from the original model and the placebo-trained model. This intermediary comparison process detects adverse changes without preventing the original model from learning from real-time data, thus maintaining adaptability while ensuring reliability through the mediating validation layer.
2Productivity
If ML models continuously learn from operational data to improve performance, then the model improves from experience, but it becomes difficult to detect and measure model degradation and adverse effects
Solution Approach 1:
The system creates a copy of the ML model and trains it on placebo data (copied training process with benign data). This copy serves as a reference model that should produce identical outputs to the original model. By comparing the copy's outputs with the original model's outputs, the system easily detects degradation without interfering with the original model's continuous learning and performance improvement.
3Productivity
If ML models are deployed in production environments to deliver business value, then the model provides operational benefits, but unpredictability in model behavior jeopardizes business continuity
Solution Approach 1:
The system performs preliminary validation on placebo-trained model copies before deploying updates to production models. This advance validation ensures that only safe model updates reach production environments, maintaining business continuity while allowing continuous delivery of business value through model improvements.
Solution Approach 2:
The system implements a feedback mechanism where validation results from placebo data injection are used to determine whether model updates should be deployed. This feedback loop ensures that production models only receive updates that pass validation, maintaining business continuity while enabling continuous value delivery through controlled model improvements.
Data Source
AI summary
Aspects of the present disclosure relate to machine learning (ML) model governance. A first ML output can be received from a first version of a ML model based on a first prompt. The first version of the ML model can be trained on placebo data to obtain a second version of the ML model. A second ML output can be received from the second version of the ML model trained on the placebo data based on the first prompt. A validation result can be received based on a comparison between the first ML output and the second ML output.