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Serverless Machine Learning with Amazon Redshift ML

Name: Serverless Machine Learning with Amazon Redshift ML
ISBN: 9781804619285

by Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi

August 2023

Intermediate to advanced

290 pages

6h 4m

English

Packt Publishing

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Serverless Machine Learning with Amazon Redshift ML
ForewordContributorsAbout the authorsAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
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 ServerlessWhat is a namespace?What is a workgroup?Connecting to your data warehouseUsing Amazon Redshift query editor v2Loading sample dataRunning your first querySummary
Chapter 2: Data Loading and Analytics on Redshift Serverless
Technical requirementsData loading using Amazon Redshift Query Editor v2Creating tablesLoading data from Amazon S3Loading data from a local driveData loading from Amazon S3 using the COPY commandLoading data from a Parquet fileAutomating file ingestion with a COPY jobBest practices for the COPY commandData loading using the Redshift Data APICreating tableLoading data using the Redshift Data APISummary
Chapter 3: Applying Machine Learning in Your Data Warehouse
Understanding the basics of MLComparing supervised and unsupervised learningClassificationRegressionTraditional steps to implement MLData preparationEvaluating an ML modelOvercoming the challenges of implementing ML todayExploring the benefits of MLApplication of ML in a data warehouseSummary
Part 2:Getting Started with Redshift ML
Chapter 4: Leveraging Amazon Redshift ML
Why Amazon Redshift ML?An introduction to Amazon Redshift MLA CREATE MODEL overviewAUTO everythingAUTO with user guidanceXGBoost (AUTO OFF)K-means (AUTO OFF)BYOMSummary
Chapter 5: Building Your First Machine Learning Model
Technical requirementsRedshift ML simple CREATE MODELUploading and analyzing the dataDiving deep into the Redshift ML CREATE MODEL syntaxCreating your first machine learning modelEvaluating model performanceChecking the Redshift ML objectivesRunning predictionsComparing ground truth to predictionsFeature importanceModel performanceSummary
Chapter 6: Building Classification Models
Technical requirementsAn introduction to classification algorithmsDiving into the Redshift CREATE MODEL syntaxTraining a binary classification model using the XGBoost algorithmEstablishing the business problemUploading and analyzing the dataUsing XGBoost to train a binary classification modelRunning predictionsPrediction probabilitiesTraining a multi-class classification model using the Linear Learner model typeUsing Linear Learner to predict the customer segmentEvaluating the model qualityRunning prediction queriesExploring other CREATE MODEL optionsSummary

Chapter 7: Building Regression Models
Technical requirementsIntroducing regression algorithmsRedshift’s CREATE MODEL with user guidanceCreating a simple linear regression model using XGBoostUploading and analyzing the dataSplitting data into training and validation setsCreating a simple linear regression modelRunning predictionsCreating multi-input regression modelsLinear Learner algorithmUnderstanding model evaluationPrediction querySummary
Chapter 8: Building Unsupervised Models with K-Means Clustering
Technical requirementsGrouping data through cluster analysisDetermining the optimal number of clustersCreating a K-means ML modelCreating a model syntax overview for K-means clusteringUploading and analyzing the dataCreating the K-means modelEvaluating the results of the K-means clusteringSummary
Part 3:Deploying Models with Redshift ML
Chapter 9: Deep Learning with Redshift ML
Technical requirementsIntroduction to deep learningBusiness problemUploading and analyzing the dataPrediction goalSplitting data into training and test datasetsCreating a multiclass classification model using MLPRunning predictionsSummary
Chapter 10: Creating a Custom ML Model with XGBoost
Technical requirementsIntroducing XGBoostIntroducing an XGBoost use caseDefining the business problemUploading, analyzing, and preparing data for trainingSplitting data into train and test datasetsPreprocessing the input variablesCreating a model using XGBoost with Auto OffCreating a binary classification model using XGBoostGenerating predictions and evaluating model performanceSummary
Chapter 11: Bringing Your Own Models for Database Inference
Technical requirementsBenefits of BYOMSupported model typesCreating the BYOM local inference modelCreating a local inference modelRunning local inference on RedshiftBYOM using a SageMaker endpoint for remote inferenceCreating BYOM remote inferenceGenerating the BYOM remote inference commandSummary
Chapter 12: Time-Series Forecasting in Your Data Warehouse
Technical requirementsForecasting and time-series dataTypes of forecasting methodsWhat is time-series forecasting?Time trending dataSeasonalityStructural breaksWhat is Amazon Forecast?Configuration and securityCreating forecasting models using Redshift MLBusiness problemUploading and analyzing the dataCreating a table with output resultsSummary
Chapter 13: Operationalizing and Optimizing Amazon Redshift ML Models
Technical requirementsOperationalizing your ML modelsModel retraining process without versioningThe model retraining process with versioningAutomating the CREATE MODEL statement for versioningOptimizing the Redshift models’ accuracyModel qualityModel explainabilityProbabilitiesUsing SageMaker Autopilot notebooksSummary
Index
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Content preview from Serverless Machine Learning with Amazon Redshift ML

Part 3:Deploying Models with Redshift ML

Part 3 introduces you to more ways to leverage Amazon Redshift ML. You will learn about deep learning algorithms, how to train a customized model, and how you can use models trained outside of Amazon Redshift to run inference queries in your data warehouse.

This part closes with an introduction to time-series forecasting, how to use it with Amazon Redshift ML, and how you can optimize and easily re-train your models.

This part comprises the following chapters:

Chapter 9, Deep Learning with Redshift ML
Chapter 10, Creating Custom ML Models with XGBoost
Chapter 11, Bring Your Own Models for In-Database Inference
Chapter 12, Time-Series Forecasting in Your Data Warehouse
Chapter 13, Operationalizing and ...

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ISBN: 9781804619285

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Serverless Machine Learning with Amazon Redshift ML

by Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi

Part 3:Deploying Models with Redshift ML

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.