English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The emerging age of connected, digital world means that there are tons ofdata, distributed to various organizations and their databases. Since this datacan be confidential in nature, it cannot always be openly shared in seek ofartificial intelligence (AI) and machine learning (ML) solutions. Instead, weneed integration mechanisms, analogous to integration patterns in informationsystems, to create multi-organization AI/ML systems. In this paper, we presenttwo real-world cases. First, we study integration between two organizations indetail. Second, we address scaling of AI/ML to multi-organization context. Thesetup we assume is that of continuous deployment, often referred to DevOps insoftware development. When also ML components are deployed in a similarfashion, term MLOps is used. Towards the end of the paper, we list the mainobservations and draw some final conclusions. Finally, we propose somedirections for future work.

MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases