The role of explainability throughout the MLOps lifecycle: review and research agenda

As Machine Learning Operations (MLOps) adoption accelerates, systematic integration of explainability is imperative for reliability, transparency, and continuous quality assurance. This paper presents a scoping review examining how explainability is integrated across the MLOps lifecycle, encompassing data handling, model development, and deployment. Each phase is further analyzed through its subareas: data handling (data quality, data pre-processing, and data management), model development (training and pre-deployment auditing), and deployment (developer oversight and end-user interfacing). We identified several key touchpoints within each subarea where XAI methods address specific technical and operational challenges. The synthesis covers a wide range of topics, from explainable imputation and data filtering to fairness auditing in high-stakes decision-making. Findings reveal that although explainability is widely applied, it remains fragmented, with insufficiently validated reliability, and limited operationalization for regulatory compliance. Building on this analysis, we propose a research agenda for embedding continuous explainability throughout MLOps pipelines. Key directions include connecting explainability touchpoints across lifecycle phases, validating the reliability of XAI methods, and operationalizing explainability to meet regulatory requirements such as those defined in the EU AI Act. By framing explainability as an infrastructural mechanism for assurance rather than a post-hoc diagnostic feature, this work advances a lifecycle-spanning perspective on trustworthy and governable AI systems.