Serverless machine learning with Amazon Redshift ML : create, train, and deploy machine learning models using familiar SQL commands
Description
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.
More Details
Format
Edition
1st edition.
Language
English
Notes
General Note
Includes index.
Description
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.
Local note
O'Reilly O'Reilly Online Learning: Academic/Public Library Edition
Table of Contents
Cover
Title page
Copyright
Dedication
Foreword
Contributors
Table of Contents
Preface
Part 1: Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning
Chapter 1: Introduction to Amazon Redshift Serverless
What is Amazon Redshift?
Getting started with Amazon Redshift Serverless
What is a namespace?
What is a workgroup?
Connecting to your data warehouse
Using Amazon Redshift query editor v2
Loading sample data
Running your first query
Summary
Chapter 2: Data Loading and Analytics on Redshift Serverless
Technical requirements
Data loading using Amazon Redshift Query Editor v2
Creating tables
Loading data from Amazon S3
Loading data from a local drive
Data loading from Amazon S3 using the COPY command
Loading data from a Parquet file
Automating file ingestion with a COPY job
Best practices for the COPY command
Data loading using the Redshift Data API
Creating table
Loading data using the Redshift Data API
Summary
Chapter 3: Applying Machine Learning in Your Data Warehouse
Understanding the basics of ML
Comparing supervised and unsupervised learning
Classification
Regression
Traditional steps to implement ML
Data preparation
Evaluating an ML model
Overcoming the challenges of implementing ML today
Exploring the benefits of ML
Summary
Part 2: Getting Started with Redshift ML
Chapter 4: Leveraging Amazon Redshift ML
Why Amazon Redshift ML?
An introduction to Amazon Redshift ML
A CREATE MODEL overview
AUTO everything
AUTO with user guidance
XGBoost (AUTO OFF)
K-means (AUTO OFF)
BYOM
Summary
Chapter 5: Building Your First Machine Learning Model
Technical requirements
Redshift ML simple CREATE MODEL
Uploading and analyzing the data
Diving deep into the Redshift ML CREATE MODEL syntax
Creating your first machine learning model
Evaluating model performance
Checking the Redshift ML objectives
Running predictions
Comparing ground truth to predictions
Feature importance
Model performance
Summary
Chapter 6: Building Classification Models
Technical requirements
An introduction to classification algorithms
Diving into the Redshift CREATE MODEL syntax
Training a binary classification model using the XGBoost algorithm
Establishing the business problem
Uploading and analyzing the data
Using XGBoost to train a binary classification model
Running predictions
Prediction probabilities
Training a multi-class classification model using the Linear Learner model type
Using Linear Learner to predict the customer segment
Evaluating the model quality
Running prediction queries
Exploring other CREATE MODEL options
Summary
Chapter 7: Building Regression Models
Citations
APA Citation, 7th Edition (style guide)
Panda, D., Bates, P., Pittampally, B., Joshi, S., & Mahony, C. (2023). Serverless machine learning with Amazon Redshift ML: create, train, and deploy machine learning models using familiar SQL commands (1st edition.). Packt Publishing Ltd..
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Debu, Panda et al.. 2023. Serverless Machine Learning With Amazon Redshift ML: Create, Train, and Deploy Machine Learning Models Using Familiar SQL Commands. Birmingham, UK: Packt Publishing Ltd.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Debu, Panda et al.. Serverless Machine Learning With Amazon Redshift ML: Create, Train, and Deploy Machine Learning Models Using Familiar SQL Commands Birmingham, UK: Packt Publishing Ltd, 2023.
Harvard Citation (style guide)Panda, D., Bates, P., Pittampally, B., Joshi, S. and Mahony, C. (2023). Serverless machine learning with amazon redshift ML: create, train, and deploy machine learning models using familiar SQL commands. 1st edn. Birmingham, UK: Packt Publishing Ltd.
MLA Citation, 9th Edition (style guide)Panda, Debu,, et al. Serverless Machine Learning With Amazon Redshift ML: Create, Train, and Deploy Machine Learning Models Using Familiar SQL Commands 1st edition., Packt Publishing Ltd., 2023.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
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| Last Update | 2025-01-24 12:33:29PM |
| Last Indexed | 2025-05-03 03:34:29AM |
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| 245 | 1 | 0 | |a Serverless machine learning with Amazon Redshift ML :|b create, train, and deploy machine learning models using familiar SQL commands /|c Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi ; foreword by Colin Mahony. |
| 250 | |a 1st edition. | ||
| 264 | 1 | |a Birmingham, UK :|b Packt Publishing Ltd.,|c 2023. | |
| 300 | |a 1 online resource (290 pages) :|b illustrations | ||
| 336 | |a text|b txt|2 rdacontent | ||
| 337 | |a computer|b c|2 rdamedia | ||
| 338 | |a online resource|b cr|2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 505 | 0 | |a Cover -- Title page -- Copyright -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning -- Chapter 1: Introduction to Amazon Redshift Serverless -- What is Amazon Redshift? -- Getting started with Amazon Redshift Serverless -- What is a namespace? -- What is a workgroup? -- Connecting to your data warehouse -- Using Amazon Redshift query editor v2 -- Loading sample data -- Running your first query -- Summary | |
| 505 | 8 | |a Chapter 2: Data Loading and Analytics on Redshift Serverless -- Technical requirements -- Data loading using Amazon Redshift Query Editor v2 -- Creating tables -- Loading data from Amazon S3 -- Loading data from a local drive -- Data loading from Amazon S3 using the COPY command -- Loading data from a Parquet file -- Automating file ingestion with a COPY job -- Best practices for the COPY command -- Data loading using the Redshift Data API -- Creating table -- Loading data using the Redshift Data API -- Summary -- Chapter 3: Applying Machine Learning in Your Data Warehouse | |
| 505 | 8 | |a Understanding the basics of ML -- Comparing supervised and unsupervised learning -- Classification -- Regression -- Traditional steps to implement ML -- Data preparation -- Evaluating an ML model -- Overcoming the challenges of implementing ML today -- Exploring the benefits of ML -- Summary -- Part 2: Getting Started with Redshift ML -- Chapter 4: Leveraging Amazon Redshift ML -- Why Amazon Redshift ML? -- An introduction to Amazon Redshift ML -- A CREATE MODEL overview -- AUTO everything -- AUTO with user guidance -- XGBoost (AUTO OFF) -- K-means (AUTO OFF) -- BYOM -- Summary | |
| 505 | 8 | |a Chapter 5: Building Your First Machine Learning Model -- Technical requirements -- Redshift ML simple CREATE MODEL -- Uploading and analyzing the data -- Diving deep into the Redshift ML CREATE MODEL syntax -- Creating your first machine learning model -- Evaluating model performance -- Checking the Redshift ML objectives -- Running predictions -- Comparing ground truth to predictions -- Feature importance -- Model performance -- Summary -- Chapter 6: Building Classification Models -- Technical requirements -- An introduction to classification algorithms | |
| 505 | 8 | |a Diving into the Redshift CREATE MODEL syntax -- Training a binary classification model using the XGBoost algorithm -- Establishing the business problem -- Uploading and analyzing the data -- Using XGBoost to train a binary classification model -- Running predictions -- Prediction probabilities -- Training a multi-class classification model using the Linear Learner model type -- Using Linear Learner to predict the customer segment -- Evaluating the model quality -- Running prediction queries -- Exploring other CREATE MODEL options -- Summary -- Chapter 7: Building Regression Models | |
| 520 | |a 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. | ||
| 590 | |a O'Reilly|b O'Reilly Online Learning: Academic/Public Library Edition | ||
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| 650 | 0 | |a Machine learning.|9 46043 | |
| 650 | 0 | |a Cloud computing.|9 78739 | |
| 700 | 1 | |a Bates, Phil,|e author. | |
| 700 | 1 | |a Pittampally, Bhanu,|e author. | |
| 700 | 1 | |a Joshi, Sumeet,|e author. | |
| 700 | 1 | |a Mahony, Colin,|e writer of foreword. | |
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