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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

A CREATE MODEL overview

The CREATE MODEL statement allows for flexibility when addressing the various use cases you may need. There are four main types of CREATE MODEL statements:

  • AUTO everything
  • AUTO with user guidance, where a user can provide the problem type
  • AUTO OFF, with customized options provided by the user
  • Bring your own model (BYOM)

Figure 4.2 illustrates the flexibility available when training models with Amazon Redshift ML:

Figure 4.2 – Amazon Redshift ML flexibility

Figure 4.2 – Amazon Redshift ML flexibility

In this chapter, we will provide an overview of the various types of CREATE MODEL statements. Subsequent chapters will provide in-depth examples of how to create all the different types of models, load the data to Redshift, and split your data into training and testing datasets.

In this section, we will walk you through the options available to create models and the optional parameters available that you can specify. All of the examples in...

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