Feature Store for Machine Learning: Curate, discover, share and serve ML features at scale
暫譯: 機器學習特徵庫:大規模策劃、發現、共享與提供機器學習特徵
J, Jayanth Kumar M.
- 出版商: Packt Publishing
- 出版日期: 2022-06-30
- 售價: $1,600
- 貴賓價: 9.5 折 $1,520
- 語言: 英文
- 頁數: 280
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1803230061
- ISBN-13: 9781803230061
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相關分類:
Machine Learning
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相關主題
商品描述
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.
商品描述(中文翻譯)
學習如何利用特徵儲存庫來充分發揮您的機器學習模型
主要特點
• 了解特徵儲存庫在機器學習(ML)生命週期中的重要性
• 探索如何共享、發現和重用特徵
• 學習如何在推論期間使特徵可用於線上模型
書籍描述
特徵儲存庫是機器學習(ML)操作中的一個儲存層,數據科學家和機器學習工程師可以在此儲存轉換和策劃的特徵,以供機器學習模型使用。這使得它們可以用於模型訓練、推論(批次和線上)以及在其他機器學習管道中的重用。了解如何充分利用特徵儲存庫可以為您節省大量時間和精力,本書將教您開始所需的所有知識。
《機器學習的特徵儲存庫》適合希望學習如何使用特徵儲存庫來共享和重用彼此的工作和專業知識的數據科學家。您將能夠實施有助於消除數據重處理、提供模型可重現能力和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剛開始接觸特徵儲存庫的開發人員提供了一些理論基礎,但對於那些準備將知識付諸實踐的人來說,還有大量的實用知識。通過實踐導向的實施和相關方法,您將能夠迅速上手。
在本書結束時,您將了解為什麼特徵儲存庫是必不可少的,以及如何在您的機器學習項目中使用它們,無論是在本地系統還是在雲端。
您將學到的內容
• 了解特徵儲存庫在機器學習管道中的重要性
• 熟悉如何使用特徵儲存庫策劃、儲存、共享和發現特徵
• 探索特徵儲存庫的不同組件和功能
• 發現如何將特徵儲存庫與批次和線上模型一起使用
• 加速您的模型生命週期並降低成本
• 部署您的第一個特徵儲存庫以用於生產案例
本書適合誰
如果您對機器學習基礎有扎實的理解,但需要全面了解特徵儲存庫以開始使用它們,那麼這本書適合您。數據/機器學習工程師和數據科學家,無論在任何領域為生產系統構建機器學習模型,支持數據工程師將機器學習模型投入生產的專業人士,以及為組織構建數據科學(ML)平台的平台工程師,都會在本書的後面章節中找到大量實用建議。
目錄大綱
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
目錄大綱(中文翻譯)
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