Building Recommendation Systems in Python and Jax Hands-On Production Systems at Scale
Building Recommendation Systems in Python and Jax Hands-On Production Systems at Scale by Bryan Bischof, published by O’Reilly Media, Incorporated on January 30, 2024, is a comprehensive guide that delves into the design and implementation of recommendation systems. This 400-page book provides practical insights into creating systems that suggest items to users, catering to various industries and scales. It covers essential concepts, mathematical foundations, and implementation details necessary for developing effective recommendation systems.
Readers will find a wealth of information on the components of recommendation systems, including relevant MLOps tools and code examples in technologies such as PySpark, SparkSQL, FastAPI, Weights & Biases, and Kafka. The book addresses critical aspects such as data framing, model evaluation, and deployment strategies, along with metrics for tracking system performance. By focusing on the practical application of these concepts, this edition serves as a valuable resource for those looking to enhance their understanding of artificial intelligence and software development in the context of business intelligence and productivity.
Official synopsis Publisher
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
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