MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases
Abstract
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.
Cite
@article{arxiv.2103.08937,
title = {MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases},
author = {Tuomas Granlund and Aleksi Kopponen and Vlad Stirbu and Lalli Myllyaho and Tommi Mikkonen},
journal= {arXiv preprint arXiv:2103.08937},
year = {2021}
}
Comments
2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) of 43rd International Conference on Software Engineering (ICSE)