The emerging age of connected, digital world means that there are tons of data, distributed to various organizations and their databases. Since this data can be confidential in nature, it cannot always be openly shared in seek of artificial intelligence (AI) and machine learning (ML) solutions. Instead, we need integration mechanisms, analogous to integration patterns in information systems, to create multi-organization AI/ML systems. In this paper, we present two real-world cases. First, we study integration between two organizations in detail. Second, we address scaling of AI/ML to multi-organization context. The setup we assume is that of continuous deployment, often referred to DevOps in software development. When also ML components are deployed in a similar fashion, term MLOps is used. Towards the end of the paper, we list the main observations and draw some final conclusions. Finally, we propose some directions for future work.

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The exponentially growing amount of digital information and data analysis increase the ability to perceive the holistic situation of people. This paper applies the digital twin paradigm to strengthen a person's ability to utilize information about themselves by creating a digital representation of their situation to support their well-being. More specifically, we propose a blueprint to empower individuals by improving their self-determination regarding their personal data. The blueprint will help service and data providers, both public and private, to develop a common understanding of the role and possibilities of a citizen's controlled personal digital twin of themselves -a Citizen Digital Twin (CDT) -for creating people-centric solutions. The blueprint also provides a rational framework for service development based on Citizen Digital Twins and serves as a basis for strategic guidance of service development. We demonstrate this with a case study of confirmation class students.

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