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Serverless Machine Learning with Amazon Redshift ML

You're reading from Serverless Machine Learning with Amazon Redshift ML Create, train, and deploy machine learning models using familiar SQL commands

Product type Paperback

Published in Aug 2023

Publisher Packt

ISBN-13 9781804619285

Length 290 pages

Edition 1st Edition

Languages

Python

Tools

Amazon Redshift

Concepts

Machine Learning

Authors (4):

Phil Bates

Sumeet Joshi

Debu Panda

Bhanu Pittampally

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 FREE CHAPTER

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

Why subscribe?

18. Other Books You May Enjoy

Technical requirements

This chapter requires a web browser and the following:

An AWS account.
An Amazon Redshift Serverless endpoint.
Amazon Redshift Query Editor v2.
Completing the Getting started with Amazon Redshift Serverless section in Chapter 1.

You can find the code used in this chapter here: https://github.com/PacktPublishing/Serverless-Machine-Learning-with-Amazon-Redshift/

Data files required for this chapter are located in a public S3 bucket: s3://packt-serverless-ml-redshift/

Let’s begin!

The rest of the chapter is locked

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Authors (4)

Debu Panda

Debu Panda

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).

See other products by Debu Panda

Sumeet Joshi

Sumeet Joshi

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.

See other products by Sumeet Joshi

Bhanu Pittampally

Bhanu Pittampally

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.

See other products by Bhanu Pittampally

Phil Bates

Phil Bates

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.

See other products by Phil Bates

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