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Serverless Machine Learning with Amazon Redshift ML

Serverless Machine Learning with Amazon Redshift ML

By : Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi
5 (3)
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Serverless Machine Learning with Amazon Redshift ML

Serverless Machine Learning with Amazon Redshift ML

5 (3)
By: Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi

Overview of this book

Amazon Redshift Serverless enables organizations to run petabyte-scale cloud data warehouses quickly and in a cost-effective way, enabling data science professionals to efficiently deploy cloud data warehouses and leverage easy-to-use tools to train models and run predictions. This practical guide will help developers and data professionals working with Amazon Redshift data warehouses to put their SQL knowledge to work for training and deploying machine learning models. The book begins by helping you to explore the inner workings of Redshift Serverless as well as the foundations of data analytics and types of data machine learning. With the help of step-by-step explanations of essential concepts and practical examples, you’ll then learn to build your own classification and regression models. As you advance, you’ll find out how to deploy various types of machine learning projects using familiar SQL code, before delving into Redshift ML. In the concluding chapters, you’ll discover best practices for implementing serverless architecture with Redshift. By the end of this book, you’ll be able to configure and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale.
Table of Contents (19 chapters)
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1
Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning
5
Part 2:Getting Started with Redshift ML
11
Part 3:Deploying Models with Redshift ML

Diving deep into the Redshift ML CREATE MODEL syntax

Since this is the first time you are going to use the CREATE MODEL syntax, let’s refresh the basic constructs of the command here.

Redshift ML provides the easy-to-use CREATE MODEL syntax to create ML models. In this section, we will focus on a simple form of the CREATE MODEL command. In later chapters, you will learn about other forms of creating model statements.

Simple CREATE MODEL is the most basic form of Redshift CREATE MODEL statement. It is geared toward the personas who are not yet ready to deal with all the intricacies of the machine learning process. This form of model creation is also used by experienced personas such as citizen data scientists for its simplicity in creating a machine learning model. Data cleaning is an essential step for any ML problem, otherwise, it follows the principle of garbage in, garbage out. Data cleaning still remains a necessary task, however, with Redshift ML data transformation...

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