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Flexibility of Modular and Accountable MLOps Pipelines for CPS

  • Operations within a Cyber Physical System (CPS) environment are naturally diverse and the resulting data sets include complex relations between sensors of the shopfloor devices setup, their configuration respectively. As Machine Learn- ing (ML) can increase the success of industrial plants in a variety of cases, like smart controlling, intrusion detection or predictive maintenance, clarifying responsibilities and operations for the whole lifecycle supports evaluating the potentially feasible scenarios. In this work, the need for highly configurable and flexible modules is demonstrated by depicting the complex possibilities of extending simple Machine Learning Operations (MLOps) pipelines with additional data sources, e.g., sensors. In addition to the particular modules core functionality, arbitrary evaluation logic or data structure specific anomaly detection can be integrated into the pipeline. With the creation of audit-trails for all operational modules, automated reports can be generated for increasing the accountability of the different physical devices and the data related processing. The concept is evaluated in the context of the project Collaborative Smart Contracting Platform for digital value-added Networks (KOSMoS), where a sensor is part of an ML pipeline and audit trails are realized using Blockchain (BC) technology.

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Author:Philipp Ruf, Christoph ReichORCiDGND, Djaffar Ould-Abdeslam
Parent Title (English):IARIA Congress 2022 : International Conference on Technical Advances and Human Consequences, July 24th - 28th, 2022, Nice, France
Document Type:Conference Proceeding
Year of Completion:2022
Release Date:2023/12/13
Tag:CPS; Deloyment; ML; MLOps; Modularization
First Page:69
Last Page:75
Licence (German):License LogoUrheberrechtlich gesch├╝tzt