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

About the author

Dr. Bryan Bischof is the Head of Data Science at Weights and Biases, and an adjunct professor in the Rutgers Master of Business and Analytics program where he teaches Data Science. He has previously built recommendation systems for clothing (at Stitch Fix), and built the world's first recommendation system for coffee (at Blue Bottle Coffee). His research interests are in geometric methods for ML, including higher order graph methods and topological features. His data visualization work appeared in the popular book The Day it Finally Happens by Mike Pearl. His Ph.D. is in pure mathematics.

Product details

• Authors: Bryan Bischof, Ph.d Bischof, Hector Yee
• Publisher: O'Reilly
• Languages: English
• Product format: Paperback / Softback
• Released: 29.12.2023
• EAN: 9781492097990
• ISBN: 978-1-4920-9799-0
• Dimensions: 178 mm x 233 mm x 15 mm
• Weight: 666 g
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; machine learning, Artificial Intelligence, COMPUTERS / Software Development & Engineering / General, COMPUTERS / Logic Design, COMPUTERS / System Administration / Storage & Retrieval, Information Retrieval, Web Services, system administration, Enterprise Software, Operational research, Computer programming / software engineering, Computer architecture and logic design, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / Machine Learning, Programming and scripting languages: general, COMPUTERS / Languages / Python, COMPUTERS / Internet / Web Services & APIs, Storage media and peripherals