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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

Business problem

We will use a wall-following robot navigation dataset to build a machine learning model using the MLP algorithm. The robot is equipped with ultrasound sensors and data is collected as the robot navigates through the room in a clockwise direction. The goal here is to guide the robot to follow the wall by giving simple directions such as Move-Forward, Slight-Right-Turn, Sharp-Right-Turn, and Slight-Left-Turn.

Since there are classes to predict for a given set of sensor readings, this is going to be a multiclass problem. We will use MLP to correctly guide the robot to follow the wall. (This data is taken from https://archive.ics.uci.edu/ml/datasets/Wall-Following+Robot+Navigation+Data and is attributed to Ananda Freire, Marcus Veloso, and Guilherme Barreto (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.)

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