Map a Use Case From Training to Inference

Home-page recommendations need to feel relevant without slowing the site. Data includes browsing history, product metadata, and purchase outcomes. Training and inference are separate system stages with separate constraints; strong designs choose which work happens offline, which is cached, and which is served live. The common shortcut is to talk about "the model" as if it does one thing in one place. That hides critical differences between offline candidate generation, cached scores, and live reranking. When teams ignore those differences, they either overbuild expensive real-time serving or underdeliver freshness where it matters most. 1. Train on historical behavior Use past…

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