Martel, YannickRoßmann, ArneSultanow, EldarWeiß, OliverWissel, MatthiasPelzel, FrankSeßler, MatthiasReussner, Ralf H.Koziolek, AnneHeinrich, Robert2021-01-272021-01-272021978-3-88579-701-2https://dl.gi.de/handle/20.500.12116/34722AI systems are increasingly evolving from laboratory experiments in data analysis to increments of productive software products. A professional AI platform must therefore not only function as a laboratory environment but must be designed and procured as a workbench for the development, productive implementation, operation and maintenance of ML models. Subsequently, it needs to integrate within a global software engineering approach. This way, Enterprise Architecture Management (EAM) must implement efficient governance of the development cycle, to enable organization-wide collaboration, to accelerate the go-live and to standardize operations. In this paper we highlight obstacles and show best practices on how architects can integrate data science and AI in their environment. Additionally, we suggest an integrated approach adapting the best practices from both the data science and DevOps.enSoftware ArchitectureEnterprise ArchitectureMachine LearningArtificial IntelligenceMLOps10.18420/inf2020_161617-5468