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 building performant ML workloads

Given the compute- and time-intensive nature of ML workloads, it is important to choose the most performant resources appropriate for each individual phase of the workload. Computation, memory, and network bandwidth requirements are unique to each phase of the ML process. Besides the performance of the infrastructure, the performance of the model as measured by metrics such as accuracy is also important. In this section, we will discuss best practices to apply in selecting the most performant resources for building ML workloads on SageMaker.

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

Rightsizing ML resources

SageMaker supports a variety of ML instance types with a varying combination of CPU, GPU, FPGA, memory, storage, and networking capacity. Each instance type, in turn, supports multiple instance sizes. So, you have a range of choices to choose from to...