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

Key Features

• Understand the significance of feature stores in the ML life cycle
• Discover how features can be shared, discovered, and re-used
• Learn to make features available for online models during inference

Book Description

Feature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed and curated features for ML models. This makes them available for model training, inference (batch and online), and reuse in other ML pipelines. Knowing how to utilize feature stores to their fullest potential can save you a lot of time and effort, and this book will teach you everything you need to know to get started.

Feature Store for Machine Learning is for data scientists who want to learn 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. With a hands-on approach to implementation and associated methodologies, you'll get up and running in no time.

By the end of this book, you'll have understood why feature stores are essential and how to use them in your ML projects, both on your local system and on the cloud.

What you will learn

• Understand the significance of feature stores in a machine learning pipeline
• Become well-versed with how to curate, store, share and discover features using feature stores
• Explore the different components and capabilities of a feature store
• Discover how to use feature stores with batch and online models
• Accelerate your model life cycle and reduce costs
• Deploy your first feature store for production use cases

Who this book is for

If you have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using them, then this book is for you. Data/machine learning engineers and data scientists who build machine learning models for production systems in any domain, those supporting data engineers in productionizing ML models, and platform engineers who build data science (ML) platforms for the organization will also find plenty of practical advice in the later chapters of this book.

學習如何運用特徵存儲庫來充分利用機器學習模型

主要特點

- 了解特徵存儲庫在機器學習生命週期中的重要性
- 發現特徵如何可以共享、發現和重複使用
- 學習如何在推理期間為在線模型提供特徵

書籍描述

特徵存儲庫是機器學習操作中的一個存儲層，數據科學家和機器學習工程師可以將轉換和精選的特徵存儲起來供機器學習模型使用。這使得這些特徵可以用於模型訓練、推理（批處理和在線）以及在其他機器學習流程中重複使用。了解如何充分利用特徵存儲庫可以節省大量時間和精力，本書將教你一切你需要知道的入門知識。

《機器學習的特徵存儲庫》適用於希望學習如何使用特徵存儲庫來共享和重複使用彼此工作和專業知識的數據科學家。你將能夠實施幫助消除數據重新處理、提供模型可重現能力以及減少工作重複的實踐，從而提高機器學習模型的生產時間。雖然這本機器學習書籍為那些剛開始接觸特徵存儲庫的開發人員提供了一些理論基礎，但對於那些準備將他們的知識付諸實踐的人來說，也有很多實用的專業知識。通過實施和相關方法的實踐方法，你將很快上手。

通過閱讀本書，你將了解到為什麼特徵存儲庫是必不可少的，以及如何在本地系統和雲端中使用它們在你的機器學習項目中。

你將學到什麼

- 了解特徵存儲庫在機器學習流程中的重要性
- 熟悉如何使用特徵存儲庫來精選、存儲、共享和發現特徵
- 探索特徵存儲庫的不同組件和功能
- 發現如何在批處理和在線模型中使用特徵存儲庫
- 加速模型生命週期並降低成本
- 部署你的第一個用於生產用例的特徵存儲庫

本書適合對機器學習基礎知識有扎實掌握，但需要全面了解特徵存儲庫以開始使用的讀者。在任何領域為生產系統構建機器學習模型的數據/機器學習工程師和數據科學家，那些在生產化機器學習模型方面支持數據工程師的人，以及為組織構建數據科學（機器學習）平台的平台工程師，在本書的後幾章中也能找到大量實用建議。

1. An Overview of the Machine Learning Life Cycle
2. What Problems Do Feature Stores Solve?
3. Feature Store Fundamentals, Terminology, and Usage
4. Adding Feature Store to ML Models
5. Model Training and Inference
6. Model to Production and Beyond
7. Feast Alternatives and ML Best Practices
8. Use Case – Customer Churn Prediction

1. 機器學習生命週期概述
2. 特徵存儲解決了哪些問題？
3. 特徵存儲基礎知識、術語和使用方法
4. 將特徵存儲添加到機器學習模型中
5. 模型訓練和推論
6. 從模型到生產以及更遠的未來
7. Feast的替代方案和機器學習最佳實踐
8. 應用案例 - 客戶流失預測