MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases
This addresses practical MLOps problems for organizations needing to collaborate on AI/ML while handling confidential data, but it is incremental as it builds on existing DevOps and integration concepts.
The paper tackles the challenge of implementing MLOps in multi-organization setups where data cannot be openly shared due to confidentiality, presenting two real-world cases to study integration and scaling without reporting specific numerical results.
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.