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Feature Store for Machine Learning: Curate, discover, share and serve ML features at scale
Learn how to leverage feature stores to make the most of your machine learning models
Purchase of the print or Kindle book includes a free PDF eBook
What’s insideInsights into the significance of feature stores in the ML life cycleEasy-to-follow techniques to show you how features can be shared, discovered, and re-usedInstructions to make features available for online models during inferenceYou’ll get the most out of this book ifYou have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using themYou’re a data/machine learning engineer or a data scientist who builds machine learning models for production systems in any domainYour role includes supporting data engineers in productionizing ML modelsYou're a platform engineer who builds data science (ML) platforms for your organizationWhat your journey will look likeThis book will show you how to utilize feature stores to their fullest potential, saving you a lot of time and effort by teaching you how to use feature stores to share and reuse each other's work and expertise.
You'll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model.
While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work.
Some of the things you’ll learn from this bookThe significance of feature stores in a machine learning pipelineHow to curate, store, share and discover features using feature storesThe different components and capabilities of a feature storeHow to use feature stores with batch and online modelsAccelerating your model life cycle and reducing costsEffective deployment of your first feature store for production use casesTable of ContentsAn Overview of the Machine Learning Life CycleWhat Problems Do Feature Stores Solve?Feature Store Fundamentals, Terminology, and UsageAdding Feature Store to ML ModelsModel Training and InferenceModel to Production and BeyondFeast Alternatives and ML Best PracticesUse Case – Customer Churn Prediction
Purchase of the print or Kindle book includes a free PDF eBook
What’s insideInsights into the significance of feature stores in the ML life cycleEasy-to-follow techniques to show you how features can be shared, discovered, and re-usedInstructions to make features available for online models during inferenceYou’ll get the most out of this book ifYou have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using themYou’re a data/machine learning engineer or a data scientist who builds machine learning models for production systems in any domainYour role includes supporting data engineers in productionizing ML modelsYou're a platform engineer who builds data science (ML) platforms for your organizationWhat your journey will look likeThis book will show you how to utilize feature stores to their fullest potential, saving you a lot of time and effort by teaching you how to use feature stores to share and reuse each other's work and expertise.
You'll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model.
While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work.
Some of the things you’ll learn from this bookThe significance of feature stores in a machine learning pipelineHow to curate, store, share and discover features using feature storesThe different components and capabilities of a feature storeHow to use feature stores with batch and online modelsAccelerating your model life cycle and reducing costsEffective deployment of your first feature store for production use casesTable of ContentsAn Overview of the Machine Learning Life CycleWhat Problems Do Feature Stores Solve?Feature Store Fundamentals, Terminology, and UsageAdding Feature Store to ML ModelsModel Training and InferenceModel to Production and BeyondFeast Alternatives and ML Best PracticesUse Case – Customer Churn Prediction
280 pages, Kindle Edition
Published June 30, 2022
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