You're reading from Machine Learning Security with Azure

Product type Book

Published in Dec 2023

Publisher Packt

ISBN-13 9781805120483

Pages 310 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Georgia Kalyva

Table of Contents (17) Chapters

Preface

1. Part 1: Planning for Azure Machine Learning Security

2. Chapter 1: Assessing the Vulnerability of Your Algorithms, Models, and AI Environments

3. Chapter 2: Understanding the Most Common Machine Learning Attacks

4. Chapter 3: Planning for Regulatory Compliance

5. Part 2: Securing Your Data

6. Chapter 4: Data Protection and Governance

7. Chapter 5: Data Privacy and Responsible AI Best Practices

8. Part 3: Securing and Monitoring Your AI Environment

9. Chapter 6: Managing and Securing Access

10. Chapter 7: Managing and Securing Your Azure Machine Learning Workspace

11. Chapter 8: Managing and Securing the MLOps Life Cycle

12. Chapter 9: Logging, Monitoring, and Threat Detection

13. Part 4: Best Practices for Enterprise Security in Azure Machine Learning

14. Chapter 10: Setting a Security Baseline for Your Azure Machine Learning Workloads

15. Index

Why subscribe?

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To get the most out of this book

To follow along with the examples in this book you will need an active Azure subscription. Knowledge about the following concepts will also be helpful in understanding the implementations presented in this book.

Basic Microsoft Azure knowledge:

An understanding of core cloud concepts, such as what cloud computing is, the differences between Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), and the benefits of using Azure cloud services.
Familiarity with the Azure Portal, which is the primary user interface for interacting with Azure services. This includes navigating the dashboard, creating and managing resources, and understanding the layout and tools available in the portal.
Familiarity with basic commands in Azure Command Line Interface (CLI) and PowerShell for managing Azure resources.

Machine learning:

An understanding of fundamental ML concepts, including supervised and unsupervised learning, along with basic algorithms such as linear regression, logistic regression, decision trees, and k-means clustering.

Programming Skills:

Basic proficiency in a programming language commonly used in data science, such as Python or R, including familiarity with libraries such as Pandas, NumPy, Scikit-learn (for Python).

Basic understanding of cybersecurity:

A basic understanding of cybersecurity involves grasping key concepts, practices, and strategies used to protect computer systems, networks, and data from cyber-attacks or unauthorized access.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.