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We examine the paradigm of data-centric artificial intelligence (DCAI) as a solution to the obstacles that small and medium-sized enterprises (SMEs) face in adopting AI. While the prevalent model-centric approach emphasizes collecting large amounts of data, SMEs often suffer from small datasets, data drift, and sparse ML knowledge, which hinders them from implementing AI. DCAI, on the other hand, emphasizes to systematically engineer the data used to build an AI system. Our contribution is to provide a concrete, transferable implementation of a DCAI development process geared towards industrial application, specifically in machining and manufacturing, and demonstrate how it enhances data quality by fostering collaboration between domain experts and ML engineers. This added value can place AI at the disposal of more SMEs. We provide the necessary background for practitioners to follow the rationale behind DCAI and successfully deploy the provided process template. |
