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Serverless Machine Learning with Amazon Redshift ML

You're reading from  Serverless Machine Learning with Amazon Redshift ML

Product type Book
Published in Aug 2023
Publisher Packt
ISBN-13 9781804619285
Pages 290 pages
Edition 1st Edition
Languages
Authors (4):
Debu Panda Debu Panda
Profile icon Debu Panda
Phil Bates Phil Bates
Profile icon Phil Bates
Bhanu Pittampally Bhanu Pittampally
Profile icon Bhanu Pittampally
Sumeet Joshi Sumeet Joshi
Profile icon Sumeet Joshi
View More author details

Table of Contents (19) Chapters

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 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

Redshift ML simple CREATE MODEL

Redshift ML simple CREATE MODEL is a feature in Amazon Redshift that allows users to create machine learning models using SQL commands, without the need for specialized skills or software. It simplifies the process of creating and deploying machine learning models by allowing users to use familiar SQL syntax to define the model structure and input data, and then automatically generates and trains the model using Amazon SageMaker. This feature can be used for a variety of machine learning tasks, including regression, classification, and clustering.

Before we dive into building the first ML model, let us set the stage by defining a problem statement that will form the basis of our model-building solution.

We are going to use a customer sales dataset to build the first machine learning model. Business leaders at the fictitious ABC Company are grappling with dwindling sales. The data team at ABC Company has performed descriptive and diagnostic analytics...

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