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

Real-time inference versus batch inference

SageMaker provides two ways to obtain inferences:

  • Real-time inference lets you get a single inference per request, or a small number of inferences, with very low latency from a live inference endpoint.
  • Batch inference lets you get a large number of inferences from a batch processing job.

Batch inference is more efficient and more cost-effective. Use it whenever your inference requirements allow. We'll explore batch inference first, and then pivot to real-time inference.

Batch inference

In many cases, we can make inferences in advance and store them for later use. For example, if you want to generate product recommendations for users on an e-commerce site, those recommendations may be based on the users' prior purchases and which products you want to promote the next day. You can generate the recommendations nightly and store them for your e-commerce site to call up when the users browse the site.