Serverless machine learning with Amazon Redshift ML : create, train, and deploy machine learning models using familiar SQL commands / Debu Panda [and four others].

Format:

Book

Author/Creator:

Panda, Debu, author.

Language:

English

Subjects (All):

Amazon Web Services (Firm).

Machine learning.

Cloud computing.

Physical Description:

1 online resource (290 pages)

Edition:

1st ed.

Place of Publication:

Birmingham, England : Packt Publishing, [2023]

Biography/History:

Panda Debu: 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). Bates Phil: 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. Pittampally Bhanu: 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. Joshi Sumeet: 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.

Summary:

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.

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

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

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

Chapter 5: Building Your First Machine Learning Model

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

Chapter 6: Building Classification Models

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

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

Chapter 7: Building Regression Models

Introducing regression algorithms

Redshift's CREATE MODEL with user guidance

Creating a simple linear regression model using XGBoost

Splitting data into training and validation sets

Creating a simple linear regression model

Creating multi-input regression models

Linear Learner algorithm

Understanding model evaluation

Prediction query

Chapter 8: Building Unsupervised Models with K-Means Clustering

Grouping data through cluster analysis

Determining the optimal number of clusters

Creating a K-means ML model

Creating a model syntax overview for K-means clustering

Creating the K-means model

Evaluating the results of the K-means clustering

Summary.

Part 3: Deploying Models with Redshift ML

Chapter 9: Deep Learning with Redshift ML

Introduction to deep learning

Business problem

Prediction goal

Splitting data into training and test datasets

Creating a multiclass classification model using MLP

Chapter 10: Creating a Custom ML Model with XGBoost

Introducing XGBoost

Introducing an XGBoost use case

Defining the business problem

Uploading, analyzing, and preparing data for training

Splitting data into train and test datasets

Preprocessing the input variables

Creating a model using XGBoost with Auto Off

Creating a binary classification model using XGBoost

Generating predictions and evaluating model performance

Chapter 11: Bringing Your Own Models for Database Inference

Benefits of BYOM

Supported model types

Creating the BYOM local inference model

Creating a local inference model

Running local inference on Redshift

BYOM using a SageMaker endpoint for remote inference

Creating BYOM remote inference

Generating the BYOM remote inference command

Chapter 12: Time-Series Forecasting in Your Data Warehouse

Forecasting and time-series data

Types of forecasting methods

What is time-series forecasting?

Time trending data

Seasonality

Structural breaks

What is Amazon Forecast?

Configuration and security

Creating forecasting models using Redshift ML

Creating a table with output results

Chapter 13: Operationalizing and Optimizing Amazon Redshift ML Models

Operationalizing your ML models.

Model retraining process without versioning

The model retraining process with versioning

Automating the CREATE MODEL statement for versioning

Optimizing the Redshift models' accuracy

Model quality

Model explainability

Probabilities

Using SageMaker Autopilot notebooks

Index

About Packt

Other Books You May Enjoy.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781804619698

1804619698

OCLC:

1396226259