Our publication “Conceptualizing the Secure Machine Learning Operations (SecMLOps) Paradigm” 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 22nd IEEE International Conference on Software Quality, Reliability, and Security (QRS) in December 2022. See Publications for more details!
Home / Publication / New Publication: Conceptualizing the Secure Machine Learning Operations (SecMLOps) Paradigm
New Publication: Conceptualizing the Secure Machine Learning Operations (SecMLOps) Paradigm