Towards an MLOps Architecture for XAI in Industrial Applications

Models get bypassed when they lack explainability: The paper identifies a critical practical problem—when deployed ML models fail to meet user expectations on accuracy and especially explainability, workers simply stop using them in real-world industrial settings, undermining the entire investment in ML systems.

Explainability is essential for trust and adoption: The authors emphasize that explanations of how ML models make decisions are not optional features but fundamental requirements for building trust and achieving acceptance among industrial users, which directly impacts whether ML systems are actually used in practice.

MLOps needs to integrate explanations from the start: Rather than treating explainability as an afterthought, the proposed architecture embeds explanation and feedback capabilities directly into the ML development and deployment processes, making interpretability a core part of the workflow rather than a separate concern.

Better explanations improve model performance: The paper notes that improved explainability leads to better identification of errors and improved model accuracy, suggesting that understanding why a model makes decisions helps teams catch and fix problems more effectively.

A practical solution exists for industrial ML: The EXPLAIN project demonstrates that a novel MLOps software architecture can successfully manage the complexity of deploying explainable ML systems across multiple real-world industrial use cases, offering a concrete path forward for responsible AI implementation.