[Context & Motivation] Machine learning (ML) use has increased significantly, [Question/Problem] however, organizations still struggle with operationalizing ML. [Principle results] In this paper, we explore the intersection between machine learning operations (MLOps) and Requirements engineering (RE) by investigating the current problems and best practices associated with developing an MLOps process. The goal is to create an artifact that would guide MLOps implementation from an RE perspective, resulting in a more systematic approach to managing ML models in production by identifying and documenting the goals and objectives. [Contribution] By establishing a more thorough understanding of how the two domains interact and by offering practical guidance for implementing MLOps processes from a RE perspective, this study advances both the MLOps and RE fields.