book

Feature Store for Machine Learning

Name: Feature Store for Machine Learning
Author: Jayanth Kumar M J
ISBN: 9781803230061

by Jayanth Kumar M J

June 2022

Beginner to intermediate

280 pages

5h 33m

English

Packt Publishing

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Feature Store for Machine Learning
ContributorsAbout the authorAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your Thoughts
Section 1 – Why Do We Need a Feature Store?
Chapter 1: An Overview of the Machine Learning Life Cycle
Technical requirementsThe ML life cycle in practiceProblem statement (plan and create)Data (preparation and cleaning)ModelPackage, release, and monitorAn ideal world versus the real worldReusability and sharingEverything in a notebookThe most time-consuming stages of MLFiguring out the datasetData exploration and feature engineeringModeling to production and monitoringSummary
Chapter 2: What Problems Do Feature Stores Solve?
Importance of features in productionWays to bring features to productionBatch model pipelineOnline model pipelineCommon problems with the approaches used for bringing features to productionRe-inventing the wheelFeature re-calculationFeature discoverability and sharingTraining vs Serving skew Model reproducibilityLow latencyFeature stores to the rescueStandardizing ML with a feature storeFeature store avoids reprocessing dataFeatures are discoverable and sharable with the feature store Serving features at low latency with feature storesPhilosophy behind feature storesSummaryFurther reading
Section 2 – A Feature Store in Action
Chapter 3: Feature Store Fundamentals, Terminology, and Usage
Technical requirementsIntroduction to Feast and installationFeast terminology and definitionsFeast initializationFeast usageRegister feature definitionsBrowsing the feature storeAdding an entity and FeatureViewGenerate training dataLoad features to the online storeFeast behind the scenesData flow in FeastSummaryFurther reading
Chapter 4: Adding Feature Store to ML Models
Technical requirementsCreating Feast resources in AWS Amazon S3 for storing dataAWS Redshift for an offline storeCreating an IAM user to access the resourcesFeast initialization for AWSExploring the ML life cycle with FeastProblem statement (plan and create)Data (preparation and cleaning)Model (feature engineering)SummaryReferences
Chapter 5: Model Training and Inference
PrerequisitesTechnical requirementsModel training with the feature storeDee's model training experimentsRam's model training experimentsModel packagingBatch model inference with FeastOnline model inference with FeastSyncing the latest features from the offline to the online storePackaging the online model as a REST endpoint with Feast codeHandling changes to the feature set during developmentStep 1 – Change feature definitionsStep 2 – Add/update schema in the Glue/Lake Formation consoleStep 3 – Update notebooks with the changesSummary Further reading
Chapter 6: Model to Production and Beyond
Technical requirementsSetting up Airflow for orchestrationS3 bucket for Airflow metadataAmazon MWAA environment for orchestrationProductionizing the batch model pipelineProductionizing an online model pipelineOrchestration of a feature engineering jobDeploying the model as a SageMaker endpointBeyond model productionFeature drift monitoring and model retrainingModel reproducibility and prediction issuesA headstart for the next modelChanges to feature definition after production Summary

Section 3 – Alternatives, Best Practices, and a Use Case
Chapter 7: Feast Alternatives and ML Best Practices
Technical requirementsThe available feature stores on the marketThe Tecton Feature StoreDatabricks Feature StoreGoogle's Vertex AI Feature StoreThe Hopsworks Feature StoreSageMaker Feature StoreFeature management with SageMaker Feature StoreResources to use SageMakerGenerating featuresDefining the feature groupFeature ingestionGetting records from an online storeQuerying historical data with Amazon AthenaCleaning up a SageMaker feature groupML best practicesData validation at sourceBreaking down ML pipeline and orchestrationTracking data lineage and versioningThe feature repositoryExperiment tracking, model versioning, and the model repositoryFeature and model monitoringMiscellaneousSummary
Chapter 8: Use Case – Customer Churn Prediction
Technical requirementsInfrastructure setupIntroduction to the problem and the datasetData processing and feature engineeringFeature group definitions and feature ingestionModel trainingModel predictionFeature monitoringModel monitoringSummary
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Content preview from Feature Store for Machine Learning

Preface

Data-driven decision-making has been the key to the success of any business, and Machine Learning (ML) plays a key role in achieving that and helping businesses stay ahead of the competition. Though ML helps in unlocking the true potential of a business, there are many obstacles along the way. According to a study, 90 percent of ML models never make it to production. The disconnect between model development and productionization as well as bad or mediocre ML practices are a few of the many reasons for this. This is why there are so many end-to-end ML platforms offering to make ML development easy. One of the primary goals of these platforms is to encourage data scientists/ML engineers to follow Machine Learning Operations (MLOps) standards ...

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Feature Engineering for Machine Learning

Publisher Resources

ISBN: 9781803230061

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Feature Store for Machine Learning

by Jayanth Kumar M J

Preface

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.