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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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Toc

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

Creating a Custom ML Model with XGBoost

So far, all of the supervised learning models we have explored have utilized the Amazon Redshift Auto ML feature, which uses Amazon SageMaker Autopilot behind the scenes. In this chapter, we will explore how to create custom machine learning (ML) models. Training a custom model gives you the flexibility to choose the model type and the hyperparameters to use. This chapter will provide examples of this modeling technique. By the end of this chapter, you will know how to create a custom XGBoost model and how to prepare the data to train your model using Redshift SQL.

In this chapter, we will go through the following main topics:

  • Introducing XGBoost
  • Introducing an XGBoost use case
  • XGBoost model with Auto off feature
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