You're reading from Amazon SageMaker Best Practices Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801070522

Length 348 pages

Edition 1st Edition

Languages

Python

Tools

Amazon SimpleDB

Concepts

Machine Learning

Authors (3):

Randy DeFauw

Shelbee Eigenbrode

Sireesha Muppala

View More author details

Table of Contents (20) Chapters

Preface

1. Section 1: Processing Data at Scale

2. Chapter 1: Amazon SageMaker Overview FREE CHAPTER

3. Chapter 2: Data Science Environments

4. Chapter 3: Data Labeling with Amazon SageMaker Ground Truth

5. Chapter 4: Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing

6. Chapter 5: Centralized Feature Repository with Amazon SageMaker Feature Store

7. Section 2: Model Training Challenges

8. Chapter 6: Training and Tuning at Scale

9. Chapter 7: Profile Training Jobs with Amazon SageMaker Debugger

10. Section 3: Manage and Monitor Models

11. Chapter 8: Managing Models at Scale Using a Model Registry

12. Chapter 9: Updating Production Models Using Amazon SageMaker Endpoint Production Variants

13. Chapter 10: Optimizing Model Hosting and Inference Costs

14. Chapter 11: Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify

15. Section 4: Automate and Operationalize Machine Learning

16. Chapter 12: Machine Learning Automated Workflows

17. Chapter 13:Well-Architected Machine Learning with Amazon SageMaker

18. Chapter 14: Managing SageMaker Features across Accounts

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Best practices for securing ML workloads

When securing an ML workload, you should take into consideration infrastructure and network security, authentication and authorization, encrypting data and model artifacts, logging and auditing, and meeting regulatory requirements. In this section, we will discuss best practices for security ML workloads using a combination of SageMaker and related AWS services.

Let's now look at best practices for securing ML workloads on AWS in the following sections.

Isolating the ML environment

To build secure ML workloads, you need an isolated compute and network environment. To achieve this for ML on SageMaker, deploy all resources such as notebooks, studio domain, training jobs, processing jobs, and endpoints within a Virtual Private Cloud (VPC). A VPC provides an isolated environment where all traffic between various SageMaker components flows within the network. You can add another layer of isolation by using security groups that include...