Adaptive Recommenders in the Real World: Inference, Evals, and System Design
Posted on Sat Sep 26 2026 | 4:30 pm
Mallika Rao explains that the true complexity of adaptive recommendation systems lies outside model architecture. She discusses how real-time feedback loops, retrieval freshness, multi-stage orchestration, and end-to-end latency budgeting enable systems to continuously learn and evolve in production under real-world operational constraints like latency, cost, and observability.
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