The role of explainability throughout the MLOps lifecycle: review and research agenda
Per Runeson
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 (trai