Learn directly from Katerina Zanos

This course does a strong job of framing recommender systems as a product and systems problem, rather than just a modeling exercise. The content is well-structured and focuses on practical decision-making across the full lifecycle from problem definition to system design and iteration. The most valuable part for me was the emphasis on metrics-first design, particularly how to build a clear and actionable metrics spec. The approach to translating product goals into measurable signals, and thinking through tradeoffs upfront, is immediately applicable and something many teams tend to underinvest in. Beyond that, the course provides a solid overview of system design patterns (retrieval, ranking, serving) and pragmatic guidance on when to use ML versus simpler approaches. It’s especially useful for aligning engineering and product perspectives, and for thinking in terms of end-to-end systems rather than isolated models. Overall, I’d recommend it to engineers and product practitioners working on recommender systems who want a more structured way to reason about building and operating production-ready systems.

Great course with comprehensive e2e approach for modern recommenders

As a product manager, this course gave me a much clearer way to think about recommender systems beyond simply picking a model and hoping it works. It really helped to see how I can turn a vague product goal into a concrete metrics spec with guardrails, and that’s something I’ll immediately apply in our roadmap planning. Katerina makes complex decisions feel practical, structured, and directly usable at work.

This course gave me a clear framework for understanding recommender systems from both a machine learning and system design perspective. It helped me connect retrieval, ranking, evaluation, and architecture choices back to the broader ML concepts I already use in my work at Google, but in a much more production-grounded way. I’d strongly recommend it to any ML engineer who wants to go deeper into RecSys without getting lost in theory.

Coming into this course as a Product Owner with no prior background in recommender systems, I was honestly worried it would be too specialized. It was not. Katerina breaks down complex concepts in a way that is practical, structured, and easy to apply even if you are new to the space. My biggest takeaway was learning to treat metrics as a control system, not just a reporting dashboard. That alone changed how I think about product decisions, tradeoffs, and experimentation. This course gave me a strong foundation and a much clearer way to think about building intelligent products.