| Description |
1 online resource (290 pages) : illustrations |
| Note |
Includes index. |
| Summary |
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. |
| Contents |
Cover -- Title page -- Copyright -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning -- Chapter 1: Introduction to Amazon Redshift Serverless -- What is Amazon Redshift? -- Getting started with Amazon Redshift Serverless -- What is a namespace? -- What is a workgroup? -- Connecting to your data warehouse -- Using Amazon Redshift query editor v2 -- Loading sample data -- Running your first query -- Summary |
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Chapter 2: Data Loading and Analytics on Redshift Serverless -- Technical requirements -- Data loading using Amazon Redshift Query Editor v2 -- Creating tables -- Loading data from Amazon S3 -- Loading data from a local drive -- Data loading from Amazon S3 using the COPY command -- Loading data from a Parquet file -- Automating file ingestion with a COPY job -- Best practices for the COPY command -- Data loading using the Redshift Data API -- Creating table -- Loading data using the Redshift Data API -- Summary -- Chapter 3: Applying Machine Learning in Your Data Warehouse |
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Understanding the basics of ML -- Comparing supervised and unsupervised learning -- Classification -- Regression -- Traditional steps to implement ML -- Data preparation -- Evaluating an ML model -- Overcoming the challenges of implementing ML today -- Exploring the benefits of ML -- Summary -- Part 2: Getting Started with Redshift ML -- Chapter 4: Leveraging Amazon Redshift ML -- Why Amazon Redshift ML? -- An introduction to Amazon Redshift ML -- A CREATE MODEL overview -- AUTO everything -- AUTO with user guidance -- XGBoost (AUTO OFF) -- K-means (AUTO OFF) -- BYOM -- Summary |
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Chapter 5: Building Your First Machine Learning Model -- Technical requirements -- Redshift ML simple CREATE MODEL -- Uploading and analyzing the data -- Diving deep into the Redshift ML CREATE MODEL syntax -- Creating your first machine learning model -- Evaluating model performance -- Checking the Redshift ML objectives -- Running predictions -- Comparing ground truth to predictions -- Feature importance -- Model performance -- Summary -- Chapter 6: Building Classification Models -- Technical requirements -- An introduction to classification algorithms |
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Diving into the Redshift CREATE MODEL syntax -- Training a binary classification model using the XGBoost algorithm -- Establishing the business problem -- Uploading and analyzing the data -- Using XGBoost to train a binary classification model -- Running predictions -- Prediction probabilities -- Training a multi-class classification model using the Linear Learner model type -- Using Linear Learner to predict the customer segment -- Evaluating the model quality -- Running prediction queries -- Exploring other CREATE MODEL options -- Summary -- Chapter 7: Building Regression Models |
| Subject |
Amazon Web Services (Firm)
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Machine learning.
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Cloud computing.
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Apprentissage automatique. |
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Infonuagique. |
| Added Author |
Bates, Phil, author.
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Pittampally, Bhanu, author.
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Joshi, Sumeet, author.
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Mahony, Colin, writer of foreword.
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