Serverless Machine Learning with Amazon Redshift ML : Create, Train, and Deploy Machine Learning Models Using Familiar SQL Commands

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 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. 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 for Data 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. 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 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 About Packt Other Books You May Enjoy Supercharge and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale * 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 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. 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. * 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 Data 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. 1. Introduction to Redshift Serverless 2. Data Loading and Analytics on Redshift Serverless 3. Applying Machine Learning in Your Data Warehouse 4. Leveraging Amazon Redshift Machine Learning 5. Building Your First Machine Learning Model 6. Building Classification Models 7. Building Regression Models 8. Building Unsupervised Models with K-Means Clustering 9. Deep Learning with Redshift ML 10. Creating Custom ML Models with XGBoost 11. Bring Your Own Models for in Database Inference 12. Time-Series Forecasting in your Data Warehouse 13. Operationalizing and Optimizing Amazon Redshift ML Models Supercharge and deploy Amazon Redshift Serverless, train and deploy Machine learning Models using Amazon Redshift ML and run inference queries at scale.Key Features Learn to build Multi-Class Classification Models Create a model, validate a model and draw conclusion from K-means clustering Learn to create a SageMaker endpoint and use that to create a Redshift ML Model for remote inferenceBook DescriptionAmazon Redshift Serverless enables organizations to run PetaBytes scales Cloud data warehouses in minutes and in most cost effective way Developers, data analysts and BI analysts can deploy cloud data warehouses and use easy-to-use tools to train models and run predictions. Developers working with Amazon Redshift data warehouses will be able to put their SQL knowledge to work with this practical guide to train and deploy Machine Learning Models. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time. Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin Deploying and Using Amazon Redshift Serverless and then dive into learning and deploying various types of Machine learning projects using familiar SQL Code. You will learn how to configure and deploy Amazon Redshift Serverless, understand the foundations of data analytics and types of data machine learning. Then you will deep dive into Redshift ML By the end of this book, you will 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 Learn how to implement an end-to-end serverless architecture for ingestion, analytics and machine learning using Redshift Serverless and Redshift ML Learn how to create supervised and unsupervised models, and various techniques to ...