Multi-Level XAI-Driven MLOps Pipeline for the Adjustment of Fruit and Vegetable Classifiers

Abstract

In this paper, we present a machine learning operations (MLOps) pipeline that exploits explainable artificial intelligence (XAI) to adjust deep neural network (DNN)-based fruit and vegetable image classifiers to data observed at super-market self-checkouts. DNNs are currently the most successful AI models for several automation tasks, including fruit and vegetable classification. However, adjusting them to on-site observations to avoid model drift is challenging, as they work as black boxes that take inputs and return results without showing the processes that lead to their responses. State-of-the-art XAI techniques could help mitigate this problem, but integrating them in MLOps pipelines is also challenging due to their high computational cost. We propose multi-task B-cos (MTBC) DNNs integrated at different pipeline levels to obtain automated information about the system’s on-site performance. MTBC DNNs allow indentification of fruit and vegetable types with contextual attributes in real time and generation of contribution maps that highlight task-relevant features for enhanced model adjustment. Experimental results show that MTBC DNNs obtain similar accuracy and performance to the equivalent baseline DNNs for fruit and vegetable classification but with the added benefit of explainability, supporting MLOps processes such as drift detection and exploratory data analysis.

Francisco Javier Iriarte

PhD Student

I am currently focusing my research in Explainable AI for model monitoring applications.

Ignacio Arganda-Carreras

Ikerbasque Research Associate Professor

My research interests include image processing, computer vision, and deep learning for biomedical applications.