Serverless Machine Learning with Amazon Redshift: 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

• 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

1. Introduction to Redshift Serverless
2. Data Loading and analytics on Redshift Serverless
3. Applying Machine Learning in Your Warehouse
4. Redshift ML Overview
5. Building your first model
6. Building classification models
7. Building Regression models
8. Building Unsupervised Models with K-Means Clustering
9. Redshift Auto ON vs Auto OFF
10. Creating models with XGBoost
11. Bring Your Own Models for in database inference
12. Bring Your Own Models for in remote endpoint invocation
13. Performance Considerations
14. Personalizing/Operationalizing

Debabrata 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)<br /><br />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.<br /><br />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 15 years. He currently lives in Frisco, TX.<br /><br />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.