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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

Grouping data through cluster analysis

So far, we have explored datasets that contained input and target variables, and we trained a model with a set of input variables and a target variable. This is called supervised learning. However, how do you address a dataset that does not contain a label to supervise the training? Amazon Redshift ML supports unsupervised learning using the cluster analysis method, also known as the K-means algorithm. In cluster analysis, the ML algorithm automatically discovers the grouping of data points. For example, if you have a population of 1,000 people, a clustering algorithm can group them based on height, weight, or age.

Unlike supervised learning, where an ML model predicts an outcome based on a label, unsupervised models use unlabeled data. One type of unsupervised learning is clustering, where unlabeled data is grouped based on its similarity or differences. From a dataset with demographic information about individuals, you can create clusters...

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