Preface

Amazon Redshift Serverless enables organizations to run petabyte-scale cloud data warehouses quickly and in a cost-effective way, enabling data science professionals to efficiently deploy cloud data warehouses and leverage easy-to-use tools to train models and run predictions. This practical guide will help developers and data professionals working with Amazon Redshift data warehouses to put their SQL knowledge to work for training and deploying machine learning models.

The book begins by helping you to explore the inner workings of Redshift Serverless as well as the foundations of data analytics and types of data machine learning. With the help of step-by-step explanations of essential concepts and practical examples, you’ll then learn to build your own classification and regression models. As you advance, you’ll find out how to deploy various types of machine learning projects using familiar SQL code, before delving into Redshift ML. In the concluding chapters, you’ll discover best practices for implementing serverless architecture with Redshift.

By the end of this book, you’ll be able to configure and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale.

Who this book is for

Data scientists and machine learning developers working with Amazon Redshift who want to explore its machine-learning capabilities will find this definitive guide helpful. A basic understanding of

machine learning techniques and working knowledge of Amazon Redshift is needed to make the most of this book.

What this book covers

Chapter 1, Introduction to Amazon Redshift Serverless, presents an overview of Amazon Redshift and Redshift Serverless, walking you through how to get started in just a few minutes and connect using Redshift Query Editor v2. You will create a sample database and run queries using the Notebook feature.

Chapter 2, Data Loading and Analytics on Redshift Serverless, helps you learn different mechanisms to efficiently load data into Redshift Serverless.

Chapter 3, Applying Machine Learning in Your Data Warehouse, introduces machine learning and common use cases to apply to your data warehouse.

Chapter 4, Leveraging Amazon Redshift Machine Learning, builds on Chapter 3. Here, we dive into Amazon Redshift ML, learning how it works and how to leverage it to solve use cases.

Chapter 5, Building Your First Machine Learning Model, sees you get hands-on with Amazon Redshift ML and build your first model using simple CREATE MODEL syntax.

Chapter 6, Building Classification Models, covers classification problems and the algorithms you can use in Amazon Redshift ML to solve these problems and learn how to create a model with user guidance.

Chapter 7, Building Regression Models, helps you identify whether a problem involves regression and explores the different methods available in Amazon Redshift ML for training and building regression models.

Chapter 8, Building Unsupervised Models with K-Means Clustering, shows you how to build machine learning models with unlabeled data and make predictions at the observation level using K-means clustering.

Chapter 9, Deep Learning with Redshift ML, covers the use of deep learning in Amazon Redshift ML using the MLP model type for data that is not linearly separable.

Chapter 10, Creating Custom ML Model with XGBoost, shows you how to use the Auto Off option of Amazon Redshift ML to prepare data in order to build a custom model.

Chapter 11, Bring Your Own Models for In-Database Inference, goes beyond Redshift ML models. Up to this point in the book, we will have run inference queries only on models built directly in Amazon Redshift ML. This chapter shows how you can leverage models built outside of Amazon Redshift ML and execute inference queries inside Amazon Redshift ML.

Chapter 12, Time-Series Forecasting in Your Data Warehouse, dives into forecasting and time-series data using the integration of Amazon Forecast with Amazon Redshift ML.

Chapter 13, Operationalizing and Optimizing Amazon Redshift ML Models, concludes the book by showing techniques to refresh your model, create versions of your models, and optimize your Amazon Redshift ML models.

To get the most out of this book

You will need access to an AWS account to perform code examples in this book. You will need either to have administrator access or to work with an administrator to create a Redshift Serverless data warehouse and the IAM user, roles, and policies used in this book.

Software/hardware covered in the book	Operating system requirements
The AWS CLI (optional)	Windows, macOS, or Linux

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Serverless-Machine-Learning-with-Amazon-Redshift. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

cnt = client.execute_statement(Database='dev',
    Sql='Select count(1) from chapter2.orders;',
    WorkgroupName=REDSHIFT_WORKGROUP)
query_id = cnt["Id"]

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

SHOW MODEL chapter5_buildfirstmodel.customer_churn_model;

Any command-line input or output is written as follows:

$ pip install pandas

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Select System info from the Administration panel.”

Tips or important notes

Appear like this.

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