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
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
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Debu Panda, lead author of the best selling EJB 3 in Action (Manning Publications), is a senior Product Management Director on the Oracle Fusion Middleware Management development team, where he drives development of the middleware management aspects of Oracle Enterprise Manager. He has more than 17 years of experience in the IT industry and has published numerous articles on enterprise Java technologies and has presented at many conferences. Debu maintains an active blog on enterprise Java and SOA technologies at http://www.debupanda.com.
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.
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 16 years. He currently lives in Frisco, TX with his wife Kavitha and daughters Vibha and Medha.
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.
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