Serverless machine learning with Amazon Redshift ML : create, train, and deploy machine learning models using familiar SQL commands /

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 w...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Panda, Debu (Autor), Bates, Phil (Autor), Pittampally, Bhanu (Autor), Joshi, Sumeet (Autor)
Otros Autores: Mahony, Colin (writer of foreword.)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing Ltd., 2023.
Edición:1st edition.
Temas:
Amazon Web Services (Firm)
Machine learning.
Cloud computing.
Apprentissage automatique.
Infonuagique.
Electronic books.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

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