New Publication: Conceptualizing the Secure Machine Learning Operations (SecMLOps) Paradigm
Our publication “” is now available online. This is the work of PhD Student, Xinrui Zhang. This paper introduces the Secure Machine Learning Operations (SecMLOps) paradigm, which extends MLOps with security considerations. We use the People, Processes, Technology, Governance and Compliance (PPTGC) framework to conceptualize SecMLOps, and to discuss challenges in adopting SecMLOps in practice. This paper aims to provide guidance and a research roadmap for ML researchers and organizational-level practitioners towards secure, reliable, and trustworthy MLOps. It was was presented at the in December 2022. See Publications for more details!