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Serverless Machine Learning with Amazon Redshift ML

You're reading from  Serverless Machine Learning with Amazon Redshift ML

Product type Book
Published in Aug 2023
Publisher Packt
ISBN-13 9781804619285
Pages 290 pages
Edition 1st Edition
Languages
Authors (4):
Debu Panda Debu Panda
Profile icon Debu Panda
Phil Bates Phil Bates
Profile icon Phil Bates
Bhanu Pittampally Bhanu Pittampally
Profile icon Bhanu Pittampally
Sumeet Joshi Sumeet Joshi
Profile icon Sumeet Joshi
View More author details

Table of Contents (19) Chapters

Preface 1. Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning
2. Chapter 1: Introduction to Amazon Redshift Serverless 3. Chapter 2: Data Loading and Analytics on Redshift Serverless 4. Chapter 3: Applying Machine Learning in Your Data Warehouse 5. Part 2:Getting Started with Redshift ML
6. Chapter 4: Leveraging Amazon Redshift ML 7. Chapter 5: Building Your First Machine Learning Model 8. Chapter 6: Building Classification Models 9. Chapter 7: Building Regression Models 10. Chapter 8: Building Unsupervised Models with K-Means Clustering 11. Part 3:Deploying Models with Redshift ML
12. Chapter 9: Deep Learning with Redshift ML 13. Chapter 10: Creating a Custom ML Model with XGBoost 14. Chapter 11: Bringing Your Own Models for Database Inference 15. Chapter 12: Time-Series Forecasting in Your Data Warehouse 16. Chapter 13: Operationalizing and Optimizing Amazon Redshift ML Models 17. Index 18. Other Books You May Enjoy

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.

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