@inproceedings{WeegerStiehlvonKistowskietal.2025, author = {Weeger, Nicolas and Stiehl, Annika and von Kistowski, J{\´o}akim and Geißels{\"o}der, Stefan and Uhl, Christian}, title = {Towards Practicable Machine Learning Development Using AI Engineering Blueprints}, series = {2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C)}, booktitle = {2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C)}, publisher = {IEEE}, doi = {10.1109/ICSA-C65153.2025.00078}, pages = {525 -- 528}, year = {2025}, abstract = {The implementation of artificial intelligence (AI) in business applications holds considerable promise for significant improvements. The development of AI systems is becoming increasingly complex, thereby underscoring the growing importance of AI engineering and MLOps techniques. Small and medium-sized enterprises (SMEs) face considerable challenges when implementing AI in their products or processes. These enterprises often lack the necessary resources and expertise to develop, deploy, and operate AI systems that are tailored to address their specific problems. Given the lack of studies on the application of AI engineering practices, particularly in the context of SMEs, this paper proposes a research plan designed to develop blueprints for the creation of proprietary machine learning (ML) models using AI engineering and MLOps practices. These blueprints enable SMEs to develop, deploy, and operate AI systems by providing reference architectures and suitable automation approaches for different types of ML. The efficacy of the blueprints is assessed through their application to a series of field projects. This process gives rise to further requirements and additional development loops for the purpose of generalization. The benefits of using the blueprints for organizations are demonstrated by observing the process of developing ML models and by conducting interviews with the developers.}, subject = {Maschinelles Lernen}, language = {en} } @inproceedings{MittelHubertDingetal.2023, author = {Mittel, Dominik and Hubert, Andreas and Ding, Junsheng and Perzylo, Alexander}, title = {Towards a Knowledge-Augmented Socio-Technical Assistance System for Product Engineering}, series = {2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)}, booktitle = {2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, doi = {10.1109/ETFA54631.2023.10275386}, pages = {1 -- 4}, year = {2023}, abstract = {Digital tools for handling the whole product engineering phase are getting more and more important in the context of Industry 4.0 and an increasing product variety. However, especially in small and medium-sized enterprises, a lot of information about product development and production is stored in different documents or isolated data silos. A promising way to arrive at a solution is to model data and knowledge with ontologies and enrich it with context information. This paper presents a concept and a showcase implementation of a company-internal and personalized assistance system for an end-to-end digital product engineering process. We combine a generic and cost-efficient human assistance solution focusing on social aspects and a company-wide knowledge graph to create a seamless and highly integrated data structure that assists many stakeholders in the product engineering process, from product designers to assembly workers. As a result, more complex products can be handled and the product engineering process can be accelerated.}, subject = {Produktentwicklung}, language = {en} }