Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands

by Debu Panda, Phil Bates, Bhanu Pittampally

Overview

Supercharge and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale

Key Features

• Leverage supervised learning to build binary classification, multi-class classification, and regression models
• Learn to use unsupervised learning using the K-means clustering method
• Master the art of time series forecasting using Redshift ML
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

• Utilize Redshift Serverless for data ingestion, data analysis, and machine learning
• Create supervised and unsupervised models and learn how to supply your own custom parameters
• Discover how to use time series forecasting in your data warehouse
• Create a SageMaker endpoint and use that to build a Redshift ML model for remote inference
• Find out how to operationalize machine learning in your data warehouse
• Use model explainability and calculate probabilities with Amazon Redshift ML

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.

Product Details

ISBN-13:	9781804619285
Publisher:	Packt Publishing
Publication date:	08/30/2023
Pages:	290
Product dimensions:	7.50(w) x 9.25(h) x 0.61(d)

About the Author

Debu Panda, a Senior Manager, Product Management at AWS, is an industry leader in analytics, application platform, and database technologies, and has more than 25 years of experience in the IT world. Debu has published numerous articles on analytics, enterprise Java, and databases and has presented at multiple conferences such as re:Invent, Oracle Open World, and Java One. He is lead author of the EJB 3 in Action (Manning Publications 2007, 2014) and Middleware Management (Packt, 2009).

Phil Bates is a Senior Analytics Specialist Solutions Architect at AWS. He has more than 25 years of experience implementing large-scale data warehouse solutions. He is passionate about helping customers through their cloud journey and leveraging the power of ML within their data warehouse.

Bhanu Pittampally is Analytics Specialist Solutions Architect at Amazon Web Services. His background is in data and analytics and is in the field for over 16 years. He currently lives in Frisco, TX with his wife Kavitha and daughters Vibha and Medha.

Sumeet Joshi is an Analytics Specialist Solutions Architect based out of New York. He specializes in building large-scale data warehousing solutions. He has over 17 years of experience in the data warehousing and analytical space.

Table of Contents

Table of Contents

1. Introduction to Redshift Serverless
2. Data Loading and Analytics on Redshift Serverless
3. Applying Machine Learning in Your Data Warehouse
4. Leveraging Amazon Redshift Machine Learning
5. Building Your First Machine Learning Model
6. Building Classification Models
7. Building Regression Models
8. Building Unsupervised Models with K-Means Clustering
9. Deep Learning with Redshift ML
10. Creating Custom ML Models with XGBoost
11. Bring Your Own Models for in Database Inference
12. Time-Series Forecasting in your Data Warehouse
13. Operationalizing and Optimizing Amazon Redshift ML Models