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)
1
Section 1: Processing Data at Scale
7
Section 2: Model Training Challenges
10
Section 3: Manage and Monitor Models
15
Section 4: Automate and Operationalize Machine Learning

Best practices for reliable ML workloads

For a reliable system, there are two considerations at the core:

  • First, the ability to recover from planned and unplanned disruptions
  • Second, the ability to meet unpredictable increases in traffic demands

Ideally, the system should achieve both without affecting downstream applications and end consumers. In this section, we will discuss best practices for building reliable ML workloads using a combination of SageMaker and related AWS services.

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

Recovering from failure

For an ML workload, the ability to recover gracefully should be part of all the steps that make up the iterative ML process. A failure can occur with data storage, data processing, model training, or model hosting, which may result from a variety of events ranging from system failure to human error.

For ML on SageMaker, all data (and model artifacts...