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Serverless Machine Learning with Amazon Redshift ML

Serverless Machine Learning with Amazon Redshift ML

By : Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi
5 (3)
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Serverless Machine Learning with Amazon Redshift ML

Serverless Machine Learning with Amazon Redshift ML

5 (3)
By: Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi

Overview of this book

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.
Table of Contents (19 chapters)
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1
Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning
5
Part 2:Getting Started with Redshift ML
11
Part 3:Deploying Models with Redshift ML

Creating forecasting models using Redshift ML

Currently, if you have to perform forecasting in your data warehouse, you need to export the dataset into external systems and then apply forecasting algorithms to create output datasets and then import them back into the data warehouse for your presentation layer or further analysis. With Redshift ML’s integration with Amazon Forecast, you don’t have to perform all these steps. You can now create the forecasting models right on your dataset within your data warehouse.

In Chapter 5, we talked about the basic CREATE MODEL syntax and its constructs. Let’s take a look at the CREATE MODEL syntax for forecasting:

CREATE MODEL forecast_model_name
FROM { table_name | ( select_query ) }
TARGET column_name
IAM_ROLE { default | 'arn:aws:iam::<AWS account-id>:role/<role-name>' }
AUTO ON MODEL_TYPE FORECAST
[ OBJECTIVE optimization_metric ]
SETTINGS (S3_BUCKET 'bucket',
   &...

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