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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

BYOM using a SageMaker endpoint for remote inference

In this section, we will explore how to create a BYOM remote inference for an Amazon SageMaker Random Cut Forest model. This means you are bringing your own machine learning model, which is trained on data outside of Redshift, and using it to make predictions on data stored in a Redshift cluster using an endpoint. In this method, to use BYOM for remote inference, a machine learning model is trained, an endpoint is created in Amazon SageMaker, and then the endpoint is accessed from within a Redshift query using SQL functions provided by the Amazon Redshift ML extension.

This method is useful when Redshift ML does not natively support models, for example, a Random Cut Forest model. You can read more about Random Cut Forest here: https://tinyurl.com/348v8nnw.

To demonstrate this feature, you will first need to follow the instructions found in this notebook (https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms...

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