Recommendation Systems (16): Industrial Architecture and Best Practices

Thu, 15 Jan 2026 09:00:00 +0000

The hardest part of a production recommendation system isn’t the model. It’s the system around the model: the feature store that prevents training/serving skew, the canary deployment that catches regressions before they hit 100M users, and the orchestration that meets a 100ms p95 latency budget while running four ML models in sequence. This final article describes the architecture that every major tech company has converged on — and the trade-offs within each layer.

Industrial Practice on Chen Kai Blog

Recommendation Systems (16): Industrial Architecture and Best Practices