  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.
