Reference Architecture of MLOps Workflows

The rapid growth in the adoption of Machine Learning Operations Workflows (MLOps WFs) has given rise to the development of numerous guidelines and tools aimed at supporting the creation and management of these WFs. However, MLOps stakeholders continue to encounter challenges in employing these guidelines and tools. Firstly, there is a lack of consensus on the standard implementation of MLOps. Secondly, the current tools only support one or a number of components within MLOps workflows, complicating their integration into end-to-end WFs. Furthermore, the tendency towards automation in MLOps has led to uncertainty about the optimal level of human involvement, raising concerns about whether complete automation is the ideal approach. Responding to these issues, our goal in this research is to aid the MLOps WF stakeholders by providing a comprehensive reference architecture, that can be consulted as a basis of consolidated knowledge and experience in designing and managing MLOps WFs.