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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
Buy this Book

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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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
1
Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning
Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning
Free Chapter
2
Chapter 1: Introduction to Amazon Redshift Serverless
Chapter 1: Introduction to Amazon Redshift Serverless
What is Amazon Redshift?
Getting started with Amazon Redshift Serverless
Connecting to your data warehouse
Summary
3
Chapter 2: Data Loading and Analytics on Redshift Serverless
Chapter 2: Data Loading and Analytics on Redshift Serverless
Technical requirements
Data loading using Amazon Redshift Query Editor v2
Data loading from Amazon S3 using the COPY command
Data loading using the Redshift Data API
Summary
4
Chapter 3: Applying Machine Learning in Your Data Warehouse
Chapter 3: Applying Machine Learning in Your Data Warehouse
Understanding the basics of ML
Traditional steps to implement ML
Overcoming the challenges of implementing ML today
Exploring the benefits of ML
Summary
5
Part 2:Getting Started with Redshift ML
Part 2:Getting Started with Redshift ML
6
Chapter 4: Leveraging Amazon Redshift ML
Chapter 4: Leveraging Amazon Redshift ML
Why Amazon Redshift ML?
An introduction to Amazon Redshift ML
A CREATE MODEL overview
Summary
7
Chapter 5: Building Your First Machine Learning Model
Chapter 5: Building Your First Machine Learning Model
Technical requirements
Redshift ML simple CREATE MODEL
Diving deep into the Redshift ML CREATE MODEL syntax
Creating your first machine learning model
Evaluating model performance
Model performance
Summary
8
Chapter 6: Building Classification Models
Chapter 6: Building Classification Models
Technical requirements
An introduction to classification algorithms
Training a binary classification model using the XGBoost algorithm
Training a multi-class classification model using the Linear Learner model type
Summary
9
Chapter 7: Building Regression Models
Chapter 7: Building Regression Models
Technical requirements
Introducing regression algorithms
Creating a simple linear regression model using XGBoost
Creating multi-input regression models
Summary
10
Chapter 8: Building Unsupervised Models with K-Means Clustering
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Chapter 8: Building Unsupervised Models with K-Means Clustering
Technical requirements
Grouping data through cluster analysis
Determining the optimal number of clusters
Creating a K-means ML model
Evaluating the results of the K-means clustering
Summary
11
Part 3:Deploying Models with Redshift ML
Part 3:Deploying Models with Redshift ML
12
Chapter 9: Deep Learning with Redshift ML
Chapter 9: Deep Learning with Redshift ML
Technical requirements
Introduction to deep learning
Business problem
Creating a multiclass classification model using MLP
Summary
13
Chapter 10: Creating a Custom ML Model with XGBoost
Chapter 10: Creating a Custom ML Model with XGBoost
Technical requirements
Introducing XGBoost
Introducing an XGBoost use case
Creating a model using XGBoost with Auto Off
Summary
14
Chapter 11: Bringing Your Own Models for Database Inference
Chapter 11: Bringing Your Own Models for Database Inference
Technical requirements
Benefits of BYOM
Creating the BYOM local inference model
BYOM using a SageMaker endpoint for remote inference
Summary
15
Chapter 12: Time-Series Forecasting in Your Data Warehouse
Chapter 12: Time-Series Forecasting in Your Data Warehouse
Technical requirements
Forecasting and time-series data
What is Amazon Forecast?
Configuration and security
Creating forecasting models using Redshift ML
Summary
16
Chapter 13: Operationalizing and Optimizing Amazon Redshift ML Models
Chapter 13: Operationalizing and Optimizing Amazon Redshift ML Models
Technical requirements
Operationalizing your ML models
Optimizing the Redshift models’ accuracy
Summary
17
Index
Index
Why subscribe?
18
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Creating a K-means ML model

In this section, we will walk through the process with the help of a use case. In this use case, assume you are a data analyst for an e-commerce company specializing in home improvement goods. You have been tasked with classifying economic segments in different regions, based on income, so that you can better target customers, based on various factors, such as median home value. We will use this dataset from Kaggle: https://www.kaggle.com/datasets/camnugent/california-housing-prices.

From this dataset, you will use the median_income, latitude, and longitude attributes so that you can create clusters based on location and income.

The syntax to create a K-means model is slightly different from what you will have used up to this point, so let’s dive into that.

Creating a model syntax overview for K-means clustering

Here is the basic syntax to create a K-means model:

CREATE model model_name
FROM (Select_statement)
FUNCTION  function_name...

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