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Design a real-time feature pipeline / feature store for online ML.

Online models need features that are fresh and computed the same way training computed them. The classic failure is training-serving skew that quietly degrades the model. The signal is the online/offline split built on one shared definition. Here is the design.

Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.

Online models need features that are fresh and computed the same way training computed them. The classic failure is training-serving skew that quietly degrades the model. The signal is the online/offline split built on one shared definition. Here is the design.

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