多组织架构中的MLOps挑战：来自两个真实案例的经验
MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases
摘要：
互联数字世界的新兴时代意味着有大量的数据分布在各种组织及其数据库中。由于这些数据在本质上是机密的，因此在寻求人工智能（AI）和机器学习（ML）解决方案时，它不能总是公开共享。相反，我们需要集成机制，类似于信息系统中的集成模式，以创建多组织AI/ML系统。在本文中，我们提出了两个现实世界的情况下。首先，我们详细研究了两个组织之间的集成。其次，我们将AI/ML扩展到多组织环境。我们假设的设置是持续部署，通常在软件开发中称为DevOps。当ML组件也以类似的方式部署时，使用术语MLOps。在论文的最后，我们列出了主要的观察结果，并得出了一些最后的结论。最后，我们提出了一些未来工作的方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
