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 cost-optimized ML workloads

For many organizations, the lost opportunity cost of not embracing disruptive technologies such as ML outweighs the ML costs. By implementing a few best practices, these organizations can get the best possible returns on their ML investment. In this section, we will discuss best practices to apply for cost-optimized ML workloads on SageMaker.

Let's now look at best practices for building cost-optimized ML workloads on AWS in the following sections.

Optimizing data labeling costs

Labeling of data used for ML training, typically done at the very beginning of the ML process, can be tedious, error-prone, and time-consuming. Labeling at scale consumes many working hours, making this an expensive task, too. To optimize cost for data labeling, use SageMaker Ground Truth. Ground Truth provides capabilities for data labeling at scale using a combination of human workforce and active learning. When active learning is enabled, a labeling...