Book Image

Amazon SageMaker Best Practices

By : Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode
Book Image

Amazon SageMaker Best Practices

By: Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode

Overview of this book

Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.
Table of Contents (20 chapters)
Section 1: Processing Data at Scale
Section 2: Model Training Challenges
Section 3: Manage and Monitor Models
Section 4: Automate and Operationalize Machine Learning

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...