#machine-learning

You defined 12 user archetypes. Your data contains 17. How hand-crafted personas fail at tens of millions of users, and what happens when you let BigQuery ML K-Means and diversity-aware sampling tell you the truth.

Gradient-boosted decision trees for people who've never trained one — how they work, when they win, when they don't, and why tabular foundation models are about to make this conversation more complicated.

The architecture behind every recommendation system that actually works at scale — why splitting the model in half is the key to serving billions of candidates in milliseconds.