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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

Summary

In this chapter, we discussed why Amazon Redshift ML is a good choice to use data in your data warehouse to make predictions.

By bringing ML to your data warehouse, Amazon Redshift ML enables you to greatly shorten the amount of time to create and train models by putting the power of ML directly in the hands of your developers, data analysts, and BI professionals.

Your data remains secure; it never leaves your VPC. Plus, you can easily control access to create and use models.

Finally, we showed you different methods of creating models in Redshift ML, such as using AUTO, how to guide model training, and an advanced method to supply hyperparameters.

Now, you understand how ML fits into your data warehouse, how to use proper security and configuration guidelines with Redshift ML, and how a model is trained in Amazon SageMaker.

In the next chapter, you will get hands-on and create your first model using Amazon Redshift ML, learn how to validate the model, and learn...

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