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Machine Learning Security with Azure

By Georgia Kalyva
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50+ new titles added every month on new and emerging tech
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Download this book in EPUB and PDF formats
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  1. Free Chapter
    Part 1: Planning for Azure Machine Learning Security
About this book
With AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure. This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture. By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.
Publication date:
December 2023
Publisher
Packt
Pages
310
ISBN
9781805120483

 

Part 1: Planning for Azure Machine Learning Security

This part is all about creating a plan to secure your resources. Security is organization-specific so you will get an overview of the Zero Trust security approach designed to secure any implementation of IT systems. You will learn to leverage the MITRE ATLAS knowledge base to understand ML attacks. Finally, you will also learn how to develop AI systems ethically and responsibly and how to use Azure services to ensure regulatory compliance.

This part has the following chapters:

  • Chapter 1, Assessing the Vulnerability of Your Algorithms, Models, and AI Environments
  • Chapter 2, Understanding the Most Common Machine Learning Attacks
  • Chapter 3, Planning for Regulatory Compliance
About the Author
  • Georgia Kalyva

    Georgia Kalyva is a technical trainer at Microsoft. She was recognized as a Microsoft AI MVP, is a Microsoft Certified Trainer, and is an international speaker with more than 10 years of experience in Microsoft Cloud, AI, and developer technologies. Her career covers several areas, ranging from designing and implementing solutions to business and digital transformation. She holds a bachelor's degree in informatics from the University of Piraeus, a master's degree in business administration from the University of Derby, and multiple Microsoft certifications. Georgia's honors include several awards from international technology and business competitions, and her journey to excellence stems from a growth mindset and a passion for technology.

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Machine Learning Security with Azure
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