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Parametry przedmiotu
- Stan
- Book Title
- Serverless Machine Learning with Amazon Redshift ML
- Title
- Serverless Machine Learning with Amazon Redshift ML
- Subtitle
- Create, train, and deploy machine learning models using familiar
- Contributor
- Colin Mahony (Foreword by)
- ISBN-10
- 1804619280
- EAN
- 9781804619285
- ISBN
- 9781804619285
- Genre
- Computing & Internet
- Release Date
- 08/30/2023
- Release Year
- 2023
- Country/Region of Manufacture
- GB
- Item Height
- 235mm
- Publication Year
- 2023
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Subject Area
- Computers, Science
- Publication Name
- Serverless Machine Learning with Amazon Redshift : Create, Train, and Deploy Machine Learning Models Using Familiar SQL Commands
- Publisher
- Packt Publishing, The Limited
- Item Length
- 92.5 in
- Subject
- General, Databases / Data Warehousing
- Item Width
- 75 in
- Number of Pages
- 384 Pages
O tym produkcie
Product Identifiers
Publisher
Packt Publishing, The Limited
ISBN-10
1804619280
ISBN-13
9781804619285
eBay Product ID (ePID)
21063194892
Product Key Features
Number of Pages
384 Pages
Publication Name
Serverless Machine Learning with Amazon Redshift : Create, Train, and Deploy Machine Learning Models Using Familiar SQL Commands
Language
English
Publication Year
2023
Subject
General, Databases / Data Warehousing
Type
Textbook
Subject Area
Computers, Science
Format
Trade Paperback
Dimensions
Item Length
92.5 in
Item Width
75 in
Additional Product Features
Intended Audience
Trade
Dewey Edition
23
Dewey Decimal
006.3
Synopsis
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. ]]>
LC Classification Number
Q336
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