A MLOps Architecture for XAI in Industrial Applications

Machine learning (ML) has become popular 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 f...

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Bibliographic Details
Main Authors: Faubel, Leonhard, Woudsma, Thomas, Methnani, Leila, Ghezeljhemeidan, Amir Ghorbani, Buelow, Fabian, Schmid, Klaus, van Driel, Willem D., Kloepper, Benjamin, Theodorou, Andreas, Nosratinia, Mohsen, Bang, Magnus
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Computer architecture
Costs
Explainable AI
Industry
Manufacturing automation
Measurement
MLOps
Production
Software
Software architecture
Software development management
XAI
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Summary:Machine learning (ML) has become popular 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 facilitate this deployment and management process. One of the MLOps challenges is understanding how ML models reason, which is key to trust and acceptance. Here, explainable AI (XAI) can help. Better error identification and improved model accuracy are only two resulting advantages. An often neglected fact is that deployed models are bypassed when model performance or explanations do not meet user expectations. In this paper, we provide a novel reference architecture to address the challenge of integrating explanations and feedback capabilities into MLOps. Our architecture is implemented in a series of industrial use cases in the project EXPLAIN. 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.
ISSN:1946-0759
DOI:10.1109/ETFA61755.2024.10711084