Serverless Machine Learning with Amazon Redshift ML
Create, train, and deploy machine learning models using familiar SQL commands
Published on 30. August 2023
Book
Paperback/Softback
290 pages
978-1-80461-928-5 (ISBN)
Description
Supercharge and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale
Key Features
Leverage supervised learning to build binary classification, multi-class classification, and regression models
Learn to use unsupervised learning using the K-means clustering method
Master the art of time series forecasting using Redshift ML
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionAmazon 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.What you will learn
Utilize Redshift Serverless for data ingestion, data analysis, and machine learning
Create supervised and unsupervised models and learn how to supply your own custom parameters
Discover how to use time series forecasting in your data warehouse
Create a SageMaker endpoint and use that to build a Redshift ML model for remote inference
Find out how to operationalize machine learning in your data warehouse
Use model explainability and calculate probabilities with Amazon Redshift ML
Who this book is forData scientists and machine learning developers working with Amazon Redshift who want to explore its machine-learning capabilities will find this definitive guide helpful. A basic understanding of machine learning techniques and working knowledge of Amazon Redshift is needed to make the most of this book.
Key Features
Leverage supervised learning to build binary classification, multi-class classification, and regression models
Learn to use unsupervised learning using the K-means clustering method
Master the art of time series forecasting using Redshift ML
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionAmazon 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.What you will learn
Utilize Redshift Serverless for data ingestion, data analysis, and machine learning
Create supervised and unsupervised models and learn how to supply your own custom parameters
Discover how to use time series forecasting in your data warehouse
Create a SageMaker endpoint and use that to build a Redshift ML model for remote inference
Find out how to operationalize machine learning in your data warehouse
Use model explainability and calculate probabilities with Amazon Redshift ML
Who this book is forData scientists and machine learning developers working with Amazon Redshift who want to explore its machine-learning capabilities will find this definitive guide helpful. A basic understanding of machine learning techniques and working knowledge of Amazon Redshift is needed to make the most of this book.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 16 mm
Weight
546 gr
ISBN-13
978-1-80461-928-5 (9781804619285)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Debu Panda | Phil Bates | Bhanu Pittampally
Serverless Machine Learning with Amazon Redshift ML
Create, train, and deploy machine learning models using familiar SQL commands
E-Book
09/2024
Packt Publishing
from
€35.99
Available for download
Persons
Debu Panda, a Senior Manager, Product Management at AWS, is an industry leader in analytics, application platform, and database technologies, and has more than 25 years of experience in the IT world. Debu has published numerous articles on analytics, enterprise Java, and databases and has presented at multiple conferences such as re:Invent, Oracle Open World, and Java One. He is lead author of the EJB 3 in Action (Manning Publications 2007, 2014) and Middleware Management (Packt, 2009). Phil Bates is a Senior Analytics Specialist Solutions Architect at AWS. He has more than 25 years of experience implementing large-scale data warehouse solutions. He is passionate about helping customers through their cloud journey and leveraging the power of ML within their data warehouse. Bhanu Pittampally is Analytics Specialist Solutions Architect at Amazon Web Services. His background is in data and analytics and is in the field for over 16 years. He currently lives in Frisco, TX with his wife Kavitha and daughters Vibha and Medha. Sumeet Joshi is an Analytics Specialist Solutions Architect based out of New York. He specializes in building large-scale data warehousing solutions. He has over 17 years of experience in the data warehousing and analytical space.
Content
Table of Contents
Introduction to Redshift Serverless
Data Loading and Analytics on Redshift Serverless
Applying Machine Learning in Your Data Warehouse
Leveraging Amazon Redshift Machine Learning
Building Your First Machine Learning Model
Building Classification Models
Building Regression Models
Building Unsupervised Models with K-Means Clustering
Deep Learning with Redshift ML
Creating Custom ML Models with XGBoost
Bring Your Own Models for in Database Inference
Time-Series Forecasting in your Data Warehouse
Operationalizing and Optimizing Amazon Redshift ML Models
Introduction to Redshift Serverless
Data Loading and Analytics on Redshift Serverless
Applying Machine Learning in Your Data Warehouse
Leveraging Amazon Redshift Machine Learning
Building Your First Machine Learning Model
Building Classification Models
Building Regression Models
Building Unsupervised Models with K-Means Clustering
Deep Learning with Redshift ML
Creating Custom ML Models with XGBoost
Bring Your Own Models for in Database Inference
Time-Series Forecasting in your Data Warehouse
Operationalizing and Optimizing Amazon Redshift ML Models