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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

Exploring the benefits of ML

There are three main areas where businesses can see the benefits of ML:

  • Increased revenue – With ML, you can leverage your data to quickly test new ideas in order to improve customer experiences. For example, using unsupervised learning, you can segment your customers and discover previously unknown purchase patterns, which can drive new focused campaigns for specific product or subscription offerings.
  • Better operational and financial efficiency – ML increases automation and agility within your business so that you can respond to changing market conditions faster. One example is forecasting product demand more accurately. By being able to better manage inventory, organizations can see huge cost savings.
  • Increased agility to respond to business risks – With ML, you can make decisions quicker than ever before. Using ML to detect anomalies, you can quickly take action when your supply chain, product quality, and other areas...

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