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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

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781804619285
Length 290 pages
Edition 1st Edition
Languages
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Authors (4):
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Phil Bates Phil Bates
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Phil Bates
Sumeet Joshi Sumeet Joshi
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Sumeet Joshi
Debu Panda Debu Panda
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Debu Panda
Bhanu Pittampally Bhanu Pittampally
Author Profile Icon Bhanu Pittampally
Bhanu Pittampally
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Table of Contents (19) Chapters Close

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 18. Other Books You May Enjoy

Diving deep into the Redshift ML CREATE MODEL syntax

Since this is the first time you are going to use the CREATE MODEL syntax, let’s refresh the basic constructs of the command here.

Redshift ML provides the easy-to-use CREATE MODEL syntax to create ML models. In this section, we will focus on a simple form of the CREATE MODEL command. In later chapters, you will learn about other forms of creating model statements.

Simple CREATE MODEL is the most basic form of Redshift CREATE MODEL statement. It is geared toward the personas who are not yet ready to deal with all the intricacies of the machine learning process. This form of model creation is also used by experienced personas such as citizen data scientists for its simplicity in creating a machine learning model. Data cleaning is an essential step for any ML problem, otherwise, it follows the principle of garbage in, garbage out. Data cleaning still remains a necessary task, however, with Redshift ML data transformation...

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