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.
翻译：连接、数字世界的新兴时代意味着向各个组织及其数据库分发大量数据。由于这些数据具有保密性质,因此在寻求人工智能和机器学习(ML)解决方案时不能总是公开分享这些数据。相反,我们需要类似于信息系统一体化模式的一体化机制,以创建多组织AI/ML系统。在本文件中,我们介绍两个现实世界的案例。首先,我们详细研究两个组织之间的一体化。第二,我们探讨将AI/ML扩大到多个组织的背景。我们假设的设置是连续部署,经常在软件开发中提及DevOps。在以类似的方式部署ML组件时,也使用MLOPs。在文件末尾,我们列出主要意见并得出一些最后结论。最后,我们提出未来工作的一些方向。
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