Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index
This addresses the practical challenge of maintaining model robustness for industry safety standards in applications like autonomous driving, though it appears incremental as it applies an existing statistical measure to a known problem.
The paper tackles the problem of detecting data distribution shifts in deployed machine learning models, specifically in computer vision for autonomous driving, by using the Population Stability Index (PSI) to measure shifts caused by noise in images, showing promising empirical results.
Safety goes first. Meeting and maintaining industry safety standards for robustness of artificial intelligence (AI) and machine learning (ML) models require continuous monitoring for faults and performance drops. Deep learning models are widely used in industrial applications, e.g., computer vision, but the susceptibility of their performance to environment changes (e.g., noise) \emph{after deployment} on the product, are now well-known. A major challenge is detecting data distribution shifts that happen, comparing the following: {\bf (i)} development stage of AI and ML models, i.e., train/validation/test, to {\bf (ii)} deployment stage on the product (i.e., even after `testing') in the environment. We focus on a computer vision example related to autonomous driving and aim at detecting shifts that occur as a result of adding noise to images. We use the population stability index (PSI) as a measure of presence and intensity of shift and present results of our empirical experiments showing a promising potential for the PSI. We further discuss multiple aspects of model monitoring and robustness that need to be analyzed \emph{simultaneously} to achieve robustness for industry safety standards. We propose the need for and the research direction toward \emph{categorizations} of problem classes and examples where monitoring for robustness is required and present challenges and pointers for future work from a \emph{practical} perspective.