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

Designing solutions for near real-time ML predictions

Sometimes machine learning applications demand high-throughput updates to features and near real-time access to the updated features. Timely access to fast-changing features is critical for the accuracy of predictions made by these applications. As an example, consider a machine learning application in a call center that predicts how to route the incoming customer calls to available agents. This application needs to have knowledge of the customer's latest web session clicks to make accurate routing decisions. If you capture a customer's web-click behavior as features, the features need to be updated instantly and the application needs access to the updated features in near-real time. Similarly, for weather prediction problems, you may want to capture the weather measurement features frequently for accurate weather predictions and need the ability to look up features in real time.

Let's look at some best practices...