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

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 inference

Book Description

Amazon 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 influence your model
Learn how to run inference queries at scale in Redshift to solve a variety of business problems using models created with Redshift ML or natively in Amazon SageMaker
Learn how to optimize your Redshift data warehouse for extreme performance
Learn how to ensure you are using proper security guidelines with Redshift ML
Learn how to use model explainability in Amazon Redshift ML, to help understand how each attribute in your training data contributes to the predicted result.

Who This Book Is For

Data Scientists and Machine Learning developers who work with Amazon Redshift and want to explore it's machine learning capabilities will find this definitive guide helpful. Basic understanding of machine learning techniques and working knowledge of Amazon Redshift is needed to get the best from this book.
Table of Contents

Introduction to Redshift Serverless
Data Loading and analytics on Redshift Serverless
Applying Machine Learning in Your Warehouse
Redshift ML Overview
Building your first model
Building classification models
Building Regression models
Building Unsupervised Models with K-Means Clustering
Redshift Auto ON vs Auto OFF
Creating models with XGBoost
Bring Your Own Models for in database inference
Bring Your Own Models for in remote endpoint invocation
Performance Considerations
Personalizing/Operationalizing