Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands

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
	Technical requirements
	Introducing regression algorithms
		Redshift’s CREATE MODEL with user guidance
	Creating a simple linear regression model using XGBoost
		Uploading and analyzing the data
		Splitting data into training and validation sets
		Creating a simple linear regression model
		Running predictions
	Creating multi-input regression models
		Linear Learner algorithm
		Understanding model evaluation
		Prediction query
	Summary
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
		Creating a model syntax overview for K-means clustering
		Uploading and analyzing the data
		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
	Technical requirements
	Introduction to deep learning
	Business problem
		Uploading and analyzing the data
		Prediction goal
		Splitting data into training and test datasets
	Creating a multiclass classification model using MLP
		Running predictions
	Summary
Chapter 10: Creating a Custom ML Model with XGBoost
	Technical requirements
	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
	Summary
Chapter 11: Bringing Your Own Models for Database Inference
	Technical requirements
	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
	Summary
Chapter 12: Time-Series Forecasting in Your Data Warehouse
	Technical requirements
	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
		Business problem
		Uploading and analyzing the data
		Creating a table with output results
	Summary
Chapter 13: Operationalizing and Optimizing Amazon Redshift ML Models
	Technical requirements
	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
	Summary
Index
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