Towards an MLOps Architecture for XAI in Industrial Applications

TL;DR

This paper presents a novel MLOps architecture designed to integrate explainability and feedback mechanisms into industrial machine learning workflows, enhancing trust, error detection, and model management.

Contribution

The paper introduces a new MLOps architecture that specifically incorporates explainability and feedback features for industrial ML applications.

Findings

Implemented in industrial use cases within the EXPLAIN project

Improves management and deployment of ML models in production

Facilitates integration of explanations into ML development processes

Abstract

Machine learning (ML) has become a popular tool in the industrial sector as it helps to improve operations, increase efficiency, and reduce costs. However, deploying and managing ML models in production environments can be complex. This is where Machine Learning Operations (MLOps) comes in. MLOps aims to streamline this deployment and management process. One of the remaining MLOps challenges is the need for explanations. These explanations are essential for understanding how ML models reason, which is key to trust and acceptance. Better identification of errors and improved model accuracy are only two resulting advantages. An often neglected fact is that deployed models are bypassed in practice when accuracy and especially explainability do not meet user expectations. We developed a novel MLOps software architecture to address the challenge of integrating explanations and feedback capabilities into the ML development and deployment processes. In the project EXPLAIN, our architecture is implemented in a series of industrial use cases. The proposed MLOps software architecture has several advantages. It provides an efficient way to manage ML models in production environments. Further, it allows for integrating explanations into the development and deployment processes.