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Machine Learning Security with Azure
Best practices for assessing, securing, and monitoring Azure Machine Learning workloads
Georgia Kalyva
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 vulnerabilities
- Gain insights into securing data, infrastructure, and workloads effectively
- Discover how to set and maintain a better security posture with the Azure Machine Learning platform
- Purchase of the print or Kindle book includes a free PDF eBook
- Explore the Azure Machine Learning project life cycle and services
- Assess the vulnerability of your ML assets using the Zero Trust model
- Explore essential controls to ensure data governance and compliance in Azure
- Understand different methods to secure your data, models, and infrastructure against attacks
- Find out how to detect and remediate past or ongoing attacks
- Explore methods to recover from a security breach
- Monitor and maintain your security posture with the right tools and best practices
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.
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