Securing Your AI and Machine Learning Systems [Video]

Securing Your AI and Machine Learning Systems

Securing Your AI and Machine Learning Systems [Video]

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 10m | 642 MB
eLearning | Skill level: All Levels

Securing Your AI and Machine Learning Systems [Video]: Design secure AI/ML solutions

Artificial Intelligence (AI) is literally eating software as more and more solutions become ML-based. Unfortunately, these systems also have vulnerabilities; but, compared to software security, few people are really knowledgeable about this area. If it’s impossible to secure AI against cyberattacks, there will be no AI-based technologies, such as self-driving cars, and yet another “AI winter” will soon be on us.

This course is almost certainly the first public, online, hands-on introduction to the future perspectives of cybersecurity and adopts a clear and easy-to-follow approach. In this course, you will learn about high-level risks targeting AI/ML systems. You will design specific security tests for image recognition systems and master techniques to test against attacks. You will then learn about various categories of adversarial attacks and how to choose the right defense strategy.

  • Design secure AI solution architectures to cover all aspects of AI security from model to environment
  • Create a high-level threat model for AI solutions and choose the right priorities against various threats
  • Design specific security tests for image recognition systems
  • Test any AI system against the latest attacks with the help of simple tools
  • Learn the most important metrics to compare various attacks and defences
  • Deploy the right defence methods to protect AI systems against attacks by comparing their efficiency
  • Secure your AI systems with the help of practical open-source tools

By the end of this course, you will be acquainted with various attacks and, more importantly, with the steps that you can take to secure your AI and machine learning systems effectively. For this course, practical experience with Python, machine learning, and deep learning frameworks is assumed, along with some basic math skills.

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