Building Recommendation Systems in Python and Jax: Hands-On Production Systems at Scale

Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way.

In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, Weights & Biases, and Kafka.

You'll learn:

• The data essential for building a RecSys
• How to frame your data and business as a RecSys problem
• Ways to evaluate models appropriate for your system
• Methods to implement, train, test, and deploy the model you choose
• Metrics you need to track to ensure your system is working as planned
• How to improve your system as you learn more about your users, products, and business case

實施和設計能夠向使用者提供建議的系統是目前最受歡迎和必不可少的機器學習應用之一。無論您希望顧客在您的網上商店中找到最吸引人的商品、豐富且娛樂他們的視頻，還是他們需要了解的新聞，推薦系統（RecSys）都提供了解決方案。

在這本實用書中，作者Bryan Bischof和Hector Yee通過核心概念和示例來幫助您為任何行業或規模創建一個RecSys。您將學習到所需的數學、思想和實施細節，以取得成功。本書包括RecSys平台組件、您堆棧中相關的MLOps工具，以及在PySpark、SparkSQL、FastAPI、Weights＆Biases和Kafka中的代碼示例和有用的建議。

您將學習到：
- 構建RecSys所需的數據
- 如何將您的數據和業務框架化為RecSys問題
- 適合您系統的模型評估方法
- 實施、訓練、測試和部署您選擇的模型的方法
- 需要跟踪的指標，以確保您的系統按計劃運作
- 在了解更多關於用戶、產品和業務案例的情況下如何改進您的系統