Integrating knowledge and expertise from designers and scientists proposes solutions to complex problems in a flexible and open-minded way. However, little insight is available in how this collaboration works. Therefore, we reflected on a research project aimed at supportive care interventions for child oncology, and detected barriers and enablers for effective designer scientist collaboration. We interviewed medical scientists (n=2), designers (n=5), health care professionals (n=2), design students (n=3), and one design innovation-expert. Enablers appeared a receptive attitude towards innovation, and shared terminology facilitated by participatory design tools, internal communication means, and common goals. Largest barrier was unstable team membership. Future collaborative research projects might benefit when preventing barriers and stimulating enablers.
The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.
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