Bridging MLOps and Infrastructure: A Workflow-Agnostic Reference Architecture for Reusable, On-Premises AI Platforms

Companies increasingly integrate Artificial Intelligence (AI) into their applications to stay competitive. However, the efficient, successful, and certifiable development of AI applications requires complex setups, including computing resources, data stores, and training pipelines. Providing developer teams with all the necessary resources, tools, and services without reinventing the setup for every project remains a significant challenge. To address this, we propose a domain- and workflow-agnostic reference architecture for an on-premises AI Platform-as-a-Service (PaaS) that supports teams throughout the entire AI lifecycle and is reusable across multiple projects. To establish a shared understanding of the functionalities that such a platform should provide, we outline a set of general platform and MLOps capabilities. We validate the proposed reference architecture against these defined capabilities. This includes evaluating its suitability for on-premises deployment, its ability to support domain- and workflow-agnostic ML development, and its capacity to manage multiple concurrent projects. Additionally, to demonstrate its practical applicability, we present a proof-of-concept implementation composed of open-source components. This implementation can serve as a starting point for teams to build customized AI platforms, potentially reducing the initial setup effort.