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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

Concepts

Machine Learning

Authors (4):

Debu Panda

Phil Bates

Bhanu Pittampally

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

Why subscribe?

18. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

accuracy 53, 91

Amazon Forecast 224, 225

model accuracy metrics 227

AmazonForecastFullAccess managed policy 225

Amazon Redshift 4

Amazon Redshift Data API 43

data loading 43, 44, 47, 48

table, creating 45-47

Amazon Redshift ML 63-65, 158

data, analyzing 80-84

data, uploading 80-84

Amazon Redshift query editor v2 14, 25, 26

used, for connecting to data warehouse 14-16

Amazon Redshift Serverless 5-8, 25

Amazon Resource Name (ARN) 242

Amazon SageMaker 9, 63, 65

Amazon SageMaker Random Cut Forest model

BYOM remote inference, creating for 213, 214

Amazon Simple Storage Service (Amazon S3) 3, 9, 68

Amazon Web Services (AWS) 5

API 6

area under the curve (AUC) 53, 54, 91

artificial intelligence (AI) 174

artificial neural network (ANN...

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Authors (4)

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

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

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

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

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