E-Books →Machine Learning Security with Azure Best practices for assessing, securing, and monitoring Azure Machine Learning workloads

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.