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Machine Learning Operations (MLOps) Security Fundamentals
"Machine Learning Operations (MLOps) Security Fundamentals" focuses on securing the end-to-end lifecycle of ML systems, from data ingestion to model deployment. Participants gain hands-on skills in protecting model integrity, controlling access to training infrastructure, and applying DevSecOps practices to AI pipelines. Cybersecurity is woven throughout to defend against threats like model inversion, data poisoning, and adversarial inference—making this training critical for operationalizing secure, scalable AI systems.
Course Overview
None
Learning Objectives
- Understand the security considerations specific to Machine Learning Operations (MLOps).
- Identify potential vulnerabilities and threats throughout the ML lifecycle.
- Learn best practices for securing ML models, data, and infrastructure.
- Implement security measures for model deployment, monitoring, and governance.
- Securing MLOps pipelines is critical to prevent adversarial attacks that could compromise AI systems and introduce cybersecurity risks.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.