The article emphasizes the increasing importance of security in machine learning, given the growing concerns as machine learning systems become more pervasive. It stresses that despite the rapid evolution of concepts like MLOps and DevOps, security remains an under-discussed topic within machine learning. The author introduces MLSecOps, which brings security considerations into the ML lifecycle. The need for robust security measures is illustrated with principles of trusted AI, highlighting that without security, all other principles such as fairness and explainability are at risk of compromise, hence warranting greater attention in the field.
security in machine learning is often overlooked despite its rising importance. There’s a need to focus on processes and human factors in securing ML systems.
the concept of MLSecOps emerged as a necessary extension of MLOps, integrating security measures into the machine learning lifecycle.
without security, principles like fairness and accountability in AI are rendered meaningless because vulnerabilities can allow unauthorized changes to models.
the Linux Foundation's working group for MLSecOps aims to highlight the critical nature of security in machine learning practices.
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