The MLOps movement adopts the DevOps objective of reducing the gaps between development and operations teams by integrating data scientist teams and Machine Learning (ML) models. In this project, we wish to apply and adapt good software engineering practices to strengthen both the overall quality of the ML model construction processes and the quality of the software systems produced, particularly in terms of extra-functional properties that will become crucial issues: Fairness, Accountability, Transparency, Ethics, and Security (FATES). The key concerns will tackle the study, formalization, measurement, and management of these properties throughout the continuous MLOps process. Indeed, more than traditional Key Performance Indicators (KPIs), such as precision and recall, are required to evaluate models' robustness in practical applications. Our project aims to study the FATES properties and, by refining proven software engineering concepts and tools, propose a systematic and tailored approach for considering those properties, particularly from the lens of ML Scientists or ML Engineers, throughout the lifecycle of the software developed following an MLOps approach.