December 2023
Intermediate to advanced
310 pages
7h 34m
English
Overview
Discover how to secure your machine learning workflows in the Azure cloud ecosystem. This book will guide you through assessing vulnerabilities, implementing robust data governance and compliance, and protecting your AI models against threats, all using best practices and the latest tools in Azure.
What this Book will help me do
- Understand common machine learning security threats and assess your workloads effectively.
- Learn techniques for securing ML model data and ensuring governance and compliance.
- Master strategies for safeguarding your Azure ML infrastructure from external attacks.
- Implement security monitoring and detection to maintain a strong security posture.
- Develop a comprehensive security plan that aligns with Azure's tools and features.
Author(s)
None Kalyva, the author, has a strong background in the intersection of artificial intelligence, machine learning, and cybersecurity. Drawing from years of experience safeguarding complex AI systems, None provides insightful and practical advice to enhance the security of ML applications in Azure.
Who is it for?
This book is ideal for machine learning developers and data scientists who work on AI projects using Azure and want to adopt best security practices. IT administrators and security engineers will also find value in securing enterprise-level ML infrastructures. Readers with intermediate knowledge of ML technologies and Azure will benefit the most, though beginners can follow the structured guidance as well.
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