Sistemas de IA Seguros en Todas las Etapas del Ciclo de Vida

Resumen

As artificial intelligence powers our world, it creates a new frontier for complex threats that standard cybersecurity practices can't handle. This course equips you with the specialized, in-demand skills to defend these critical systems from end to end.

You will learn to think like an attacker, identifying unique threats like data poisoning, adversarial evasion, and model inference attacks. We'll journey through the entire MLOps lifecycle, pinpointing vulnerabilities from the moment data is collected to the second a model is deployed.

But this isn't just theory—you will immediately apply your knowledge in a series of hands-on labs. Using the industry-standard MITRE ATLAS framework, you'll perform a full threat model analysis on a sample AI application.

You will then implement practical, code-based mitigation strategies to build more resilient systems, culminating your learning in a final project where you conduct a full security audit. This course is ideal for AI engineers, data scientists, cybersecurity professionals, and anyone involved in the design, development, or deployment of AI systems.

It is especially valuable for professionals working in sectors where security is a priority, such as healthcare, finance, and government. Learners should have a foundational understanding of AI, machine learning, and basic cybersecurity concepts.

Familiarity with software development practices and system architecture will be beneficial, but not required. By the end of this course, you will have the confidence and tangible skills to protect the next generation of technology and become an essential asset in the world of AI security.

Programa

• El panorama de amenazas de IA
Este módulo presenta a los estudiantes el panorama de la seguridad de la IA. Desglosa las principales categorías de ataques que afectan a los sistemas de IA e introduce marcos fundamentales para comprender y clasificar estas amenazas emergentes.
• Creación y seguimiento de modelos con MLflow
Este módulo se centra en el uso de MLflow para el seguimiento de experimentos y la gestión de modelos, un componente crítico de MLOps en Databricks.
• Despliegue y gestión de modelos
Este módulo concluye el ciclo de vida de ML al cubrir el despliegue y la gestión de modelos utilizando el MLflow Model Registry.

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Impartido por

Ashish Mohan and Starweaver

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Materias

Computer Science