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EbookBell Team
4.3
28 reviewsSupercharge 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
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