Machine Learning Security with Azure: Best practices for assessing, securing, and monitoring Azure Machine Learning workloads
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Implement industry best practices to identify vulnerabilities and protect your data, models, environment, and applications while learning how to recover from a security breach
Key Features:
Learn about machine learning attacks and assess your workloads for vulnerabilitiesGain insights into securing data, infrastructure, and workloads effectivelyDiscover how to set and maintain a better security posture with the Azure Machine Learning platformPurchase of the print or Kindle book includes a free PDF eBook
Book Description:
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 access 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.
What You Will Learn:
Explore the Azure Machine Learning project life cycle and servicesAssess the vulnerability of your ML assets using the Zero Trust modelExplore essential controls to ensure data governance and compliance in AzureUnderstand different methods to secure your data, models, and infrastructure against attacksFind out how to detect and remediate past or ongoing attacksExplore methods to recover from a security breachMonitor and maintain your security posture with the right tools and best practices
Who this book is for:
Machine learning book; Ai and machine learning for coders; Cybersecurity; Hand-on machine learning; Cybersecurity books
This book is for anyone looking to learn how to assess, secure, and monitor every aspect of AI or machine learning projects running on the Microsoft Azure platform using the latest security and compliance, industry best practices, and standards. This is a must-have resource for machine learning developers and data scientists working on ML projects. IT administrators, DevOps, and security engineers required to secure and monitor Azure workloads will also benefit from this book, as the chapters cover everything from implementation to deployment, AI attack prevention, and recovery.
Publisher : Packt Publishing (December 28, 2023)
Language : English
Paperback : 310 pages
ISBN-10 : 1805120484
ISBN-13 : 978-1805120483
Item Weight : 1.21 pounds
Dimensions : 9.25 x 7.52 x 0.65 inches
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4 reviews for Machine Learning Security with Azure: Best practices for assessing, securing, and monitoring Azure Machine Learning workloads
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H2N –
Excellent book
This Azure Machine Learning book was discussed how to to secure their projects. The book covers from Azure ML basics, common ML threats, to implementing robust security measures. The audiences could be developers, data scientists, IT, and security professional. Vulnerability assessment, ML attacks, regulatory compliance, data governance, and responsible AI are covered. It also explores MLOps, monitoring, and threat detection with a solid foundation for developing a security-focused mindset and ensuring ethical AI development.
Om S –
Security Measures for Azure Machine Learning
Discover the ins and outs of safeguarding your machine learning projects on Microsoft Azure with ‘Machine Learning Security with Azure.’ This practical guide breaks down common threats and provides straightforward measures to ensure the security of your data, models, and infrastructure. From understanding machine learning attacks to implementing responsible AI practices, the book introduces the Zero Trust model for vulnerability assessment. With a focus on accessible language, it’s an invaluable resource for developers, data scientists, IT administrators, and security engineers working on Azure machine learning projects.
Blackman –
Excellent
The book is exactly what someone who wants to secure their ML workloads on Azure needs.The flow and the writing make it easily to follow from start to finish. This book can work also as a checklist for people who work on Ml on Azure. You can have it next to you. And security teams can follow the recommendations to keep their organizationâs workloads hardened and secure.
Maria Melicio –
I found this book to be an excellent resource for anyone looking to learn more about machine learning security. The author does a great job of explaining the various types of attacks that can be launched against machine learning models, and provides practical guidance on how to assess and secure your workloads. The book is well-organized and easy to follow, with plenty of real-world examples and best practices. I particularly appreciated the sections on data security and model security, which are often overlooked in other resources. Overall, I highly recommend this book to anyone looking to improve their machine learning security skills.