The EXPLAIN project (EXPLanatory interactive Artificial intelligence for INdustry) aims at enabling explainable Machine Learning in industry. MLOps (Machine Learning Operations) includes tools, practices, and processes for deploying ML (Machine Learning) in production. These will be extended by explainability methods as part of the project.

This study aims to determine to what extent MLOps is implemented by four project partner companies. Further, the study describes the ML use cases, MLOps software architecture, tools, and requirements in the companies perspective. Besides, requirements for a novel MLOps software architecture, including explainability methods, are collected.

As a result the interviews show that each of the interviewed industry partners use MLOps differently. Different tools and architectural patterns are used depending on the particular use case. Overall, most information we gathered focused on architecture decisions in the MLOps tool landscape used by the interviewed companies.