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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

accuracy 53, 91

Amazon Forecast 224, 225

model accuracy metrics 227

AmazonForecastFullAccess managed policy 225

Amazon Redshift 4

Amazon Redshift Data API 43

data loading 43, 44, 47, 48

table, creating 45-47

Amazon Redshift ML 63-65, 158

data, analyzing 80-84

data, uploading 80-84

Amazon Redshift query editor v2 14, 25, 26

used, for connecting to data warehouse 14-16

Amazon Redshift Serverless 5-8, 25

Amazon Resource Name (ARN) 242

Amazon SageMaker 9, 63, 65

Amazon SageMaker Random Cut Forest model

BYOM remote inference, creating for 213, 214

Amazon Simple Storage Service (Amazon S3) 3, 9, 68

Amazon Web Services (AWS) 5

API 6

area under the curve (AUC) 53, 54, 91

artificial intelligence (AI) 174

artificial neural network (ANN...

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