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How to build production-ready Recommender Systems

theneuralmaze.substack.com · 1,428 words · saved by 1 readers

Since I started working as a Machine Learning Engineer, I’ve worked on all kinds of problems: time series forecasting, computer vision tasks, speech recognition systems, price optimization algorithms, etc. But if I’m being honest, the ones I’ve enjoyed the most are Recommender Systems. Whether it’s for banks or fashion retailers, I love seeing how these systems directly impact customers, making their experiences smoother and more personalised. That’s why, today, I want to share a powerful framework you should always keep in mind when building real-world Recommender Systems: the 4-stage design. This design was first introduced by Even Oldridge and Karl Byleen-Higley three years ago as an improvement over the earlier 2-stage design, which Eugene Yan described in this article. The 4-stage design has four main stages / steps (obvious, right? 🤣). Let’s break them down! Think about YouTube - do you think its recommender system scores every single video for every user? No way. That would be

How to build production-ready Recommender Systems An overview of the 4-stage Recommender System Design Miguel Otero Pedrido Mar 19, 2025 159 15 21 Share Image by freestocks (source: Unsplash ) Since I started working as a Machine Learning Engineer , I’ve worked on all kinds of problems: time series forecasting, computer vision tasks, speech recognition systems, price optimization algorithms, etc. But if I’m being honest, the ones I’ve enjoyed the most are Recommender Systems . Whether it’s for banks or fashion retailers, I love seeing how these systems directly impact customers, making their e

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