Building recommendation systems in Python and Jax

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