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

Machine learning use case and dataset

Throughout this book, we will be using examples to demonstrate the best practices that apply across the ML life cycle. For this, we'll focus on a single ML use case and use an open dataset with data relating to the ML use case.

The primary use case we'll explore in this book is predicting air quality readings. Given a location (weather station) and date, we'll try to predict a value for a particular type of air quality measurement (for example, pm25 or o3). We'll treat this as a regression problem and explore XGBoost and neural network-based model approaches.

For this, we'll use a dataset from OpenAQ ( that includes air quality data from public data sources. The dataset that we will use is the realtime dataset ( and the realtime-parquet-gzipped dataset (, which includes daily...