Building Recommendation Systems in Python and JAX 1st Edition

Capture the future of personalized Books and services with Building Recommendation Systems in Python and JAX, 1st Edition by Bryan Bischof, Ph.D. and Hector Yee. This authoritative guide walks you from core recommender theory to modern, production-ready implementations using Python and the high-performance JAX library.

Start with a clear, engaging overview of collaborative filtering, content-based methods, embeddings, and deep ranking architectures. Progress through practical chapters that demystify model design, loss functions, evaluation metrics, and scalability considerations for real-world datasets. Written for data scientists, ML engineers, and applied researchers, the book balances accessible explanations with rigorous detail.

What sets this edition apart is its focus on performance and deployment: learn how JAX accelerates training, enables efficient differentiation, and scales models for large catalogs. Packed with intuitive diagrams and hands-on examples, the book helps you translate concepts into working systems that improve relevance, engagement, and business metrics.

Whether you’re building recommendation engines for e-commerce, media, or B2B services, you’ll gain actionable techniques for implementing ranking pipelines, handling cold-starts, and measuring impact. Ideal for practitioners in North America, Europe, Asia, and beyond, the text equips teams to deploy robust, explainable recommenders in production environments.

If you want a practical, up-to-date resource that bridges theory and engineering, Building Recommendation Systems in Python and JAX is an essential addition to your library. Order your copy today and accelerate your journey to building smarter, faster, and more personalized systems.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.