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Serverless Machine Learning with Amazon Redshift ML

By Debu Panda , Phil Bates , Bhanu Pittampally and 1 more
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  1. Free Chapter
    Chapter 1: Introduction to Amazon Redshift Serverless
About 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.
Publication date:
August 2023
Publisher
Packt
Pages
290
ISBN
9781804619285

 

Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning

Reaping the benefits of machine learning across an organization requires access to data, easy-to-use tools, and built-in algorithms that anyone can use no matter their level of experience with machine learning.

Part 1 shows how easy it is to get started with Amazon Redshift Serverless and Amazon Redshift ML without having to manage data warehouse infrastructure.

By the end of Part 1, you will know how to run queries using Query Editor v2 notebooks and different techniques for loading data into Amazon Redshift Serverless. You will then be introduced to machine learning and gain an understanding of how you can use machine learning in your data warehouse.

This part comprises the following chapters:

  • Chapter 1, Introduction to Amazon Redshift Serverless
  • Chapter 2, Data Loading and Analytics on Redshift Serverless
  • Chapter 3, Applying Machine Learning in Your...
About the Authors
  • Debu Panda

    Debu Panda, a Senior Manager, Product Management at AWS, is an industry leader in analytics, application platform, and database technologies, and has more than 25 years of experience in the IT world. Debu has published numerous articles on analytics, enterprise Java, and databases and has presented at multiple conferences such as re:Invent, Oracle Open World, and Java One. He is lead author of the EJB 3 in Action (Manning Publications 2007, 2014) and Middleware Management (Packt, 2009).

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  • Phil Bates

    Phil Bates is a Senior Analytics Specialist Solutions Architect at AWS. He has more than 25 years of experience implementing large-scale data warehouse solutions. He is passionate about helping customers through their cloud journey and leveraging the power of ML within their data warehouse.

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  • Bhanu Pittampally

    Bhanu Pittampally is Analytics Specialist Solutions Architect at Amazon Web Services. His background is in data and analytics and is in the field for over 16 years. He currently lives in Frisco, TX with his wife Kavitha and daughters Vibha and Medha.

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  • Sumeet Joshi

    Sumeet Joshi is an Analytics Specialist Solutions Architect based out of New York. He specializes in building large-scale data warehousing solutions. He has over 17 years of experience in the data warehousing and analytical space.

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Serverless Machine Learning with Amazon Redshift ML
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