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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

Building Your First Machine Learning Model

In the previous chapter, you learned about Redshift Machine Learning (ML) benefits such as eliminating data movement and how models can be created using simple Structured Query Language (SQL) commands.

In this chapter, you are going to build your first machine learning model by using the standard SQL dialect. Amazon Redshift makes it very easy to use familiar SQL dialect to train, deploy, and run inferences against machine learning models. This approach makes it easy for different data personas, for example, database developers, database engineers, and citizen data scientists, to train and build machine learning models without moving data outside of their data warehouse platform and without having to learn a new programming language.

In this chapter, you will learn about using Amazon Redshift ML simple CREATE MODEL, which uses the Amazon SageMaker Autopilot framework behind the scenes, to create your first model. You will also learn...

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