English | Size: 55.69 MB
Security is paramount in Azure AI development, due to the vulnerability of machine learning models to adversarial attacks on training data. To mitigate these risks, it’s crucial to validate and sanitize training data, employ secure data transfer protocols, and protect sensitive or personal information within training data. In this course, programmer and UI designer Walt Ritscher presents useful security tips for developers. Understand the significance of security in Azure AI development and its implications for machine learning models. Go over techniques for validating and sanitizing training data to protect against adversarial attacks and maintain model accuracy. Explore secure data transfer protocols to ensure the integrity and confidentiality of data during system interactions. Plus, find out how to implement auditing, logging, and monitoring systems for anomaly detection, security maintenance, and regulatory compliance in Azure AI projects.
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