Many developed machine learning models are not used in production applications as several challenges must be solved to develop and deploy ML models. Manual reimplementation and heterogeneous environments increase the effort required to develop an ML model or improve an existing one, considerably slowing down the overall process. Furthermore, it is required that a model is constantly monitored to ensure high-quality predictions and avoid possible drifts or biases. MLOps processes solve these challenges and streamline the development and deployment process by covering the whole life cycle of ML models. Even if the research area of MLOps, which applies DevOps principles to ML models, is relatively new, several researchers have already developed abstract MLOps process models. Research for cases with multiple collaboration partners is rare. This research project aims to develop an MLOps process for cases involving multiple collaboration partners. Hence, a case study is conducted with the cooperation of Aimo and LNU as a single case. Aimo requires ML models for their application and collaborates with LNU regarding this demand. LNU develops ML models based on the provided data, which Aimo integrates into their application afterward. This case is analyzed in-depth to identify challenges and the current process. These results are required to elaborate a suitable MLOps process for the case, which also considers the handover of artifacts between the collaboration partners. This process is derived from the already existing general MLOps process models. It is also instantiated to generate a benefit for the case and evaluate the feasibility of the MLOps process. Required components are identified, and existing MLOps tools are collected and compared, leading to the selection of suitable tools for the case. A project template is implemented and applied to an ML model project of the case to show the feasibility. As a result, this research project provides a concrete MLOps process. Besides that, several artifacts were elaborated, such as a project template for ML models in which the selected toolset is applied. These results mainly fit the analyzed case. Nevertheless, several findings are also generalizable such as the identified challenges. The compared alternatives and the generally applied method to elaborate an MLOps process can also be applied to other settings. This is also the case for several artifacts of this project, such as the tool comparison table and the applied process to select suitable tools. This case study shows that it is possible to set up MLOps processes with a high maturity level in situations where multiple cooperation partners are involved and artifacts need to be transferred among them.