Training a recommendation model is 20% of the work.

Serving it is the other 80%.

I just published a deep dive on the Multi-Stage Funnel—the architecture behind every modern recommender system:

→ Two-Tower models that turn search into geometry

→ Vector databases that find needles in billion-item haystacks

→ Cross-encoders that capture the "chemistry" between users and items → Feature stores that keep everything running in real-time

If you've ever wondered how recommendations actually work at scale—not the ML theory, but the engineering—this one's for you.