Serverless Machine Learning with Amazon Redshift ML: 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:

• 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

Book 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

Who this book is for:

Data scientists and machine learning developers working with Amazon Redshift who want to explore its machine-learning capabilities will find this definitive guide helpful. A basic understanding of machine learning techniques and working knowledge of Amazon Redshift is needed to make the most of this book.

加速並部署 Amazon Redshift Serverless，使用 Amazon Redshift ML 訓練和部署機器學習模型，並以大規模運行推論查詢。

主要特點：
- 利用監督式學習建立二元分類、多類別分類和回歸模型
- 學習使用 K-means 聚類方法進行非監督式學習
- 掌握使用 Redshift ML 進行時間序列預測的技巧
- 購買印刷版或 Kindle 版本的書籍將包含免費的 PDF 電子書

書籍描述：
Amazon Redshift Serverless 讓組織能夠快速且具有成本效益地運行 PB 級的雲數據倉庫，使數據科學專業人員能夠高效地部署雲數據倉庫並利用易於使用的工具來訓練模型和運行預測。本實用指南將幫助開發人員和與 Amazon Redshift 數據倉庫合作的數據專業人員將他們的 SQL 知識應用於訓練和部署機器學習模型。

本書首先幫助您探索 Redshift Serverless 的內部運作方式以及數據分析的基礎知識和數據機器學習的類型。通過逐步解釋基本概念並提供實際示例，您將學習如何構建自己的分類和回歸模型。隨著進一步的學習，您將了解如何使用熟悉的 SQL 代碼部署各種類型的機器學習項目，然後深入研究 Redshift ML。在結尾章節中，您將發現使用 Redshift 實現無服務架構的最佳實踐。

通過閱讀本書，您將能夠配置和部署 Amazon Redshift Serverless，使用 Amazon Redshift ML 訓練和部署機器學習模型，並以大規模運行推論查詢。

學到的內容：
- 利用 Redshift Serverless 進行數據輸入、數據分析和機器學習
- 創建監督式和非監督式模型，並學習如何提供自定義參數
- 了解如何在數據倉庫中使用時間序列預測
- 創建 SageMaker 端點並使用它來構建遠程推論的 Redshift ML 模型
- 了解如何在數據倉庫中實現機器學習操作
- 使用模型可解釋性並使用 Amazon Redshift ML 計算概率

本書適合對 Amazon Redshift 機器學習功能感興趣的數據科學家和機器學習開發人員。需要具備基本的機器學習技術理解和 Amazon Redshift 的工作知識，以充分利用本書的內容。