How would you design an A/B test for two live ML models?
Tests production experimentation rigor beyond random splitting. Strong answers cover: consistent user hashing for sticky assignment, isolated feature stores, guardrail metrics, and pre-calculated statistical power.
Tests whether you can build a fair, production-grade model experimentation platform rather than just splitting traffic. A strong answer outlines: deterministic user bucketing for session consistency; fully isolated feature engineering and serving pipelines per variant; automated guardrail metrics like latency, error rate, and prediction drift; and a power analysis to fix sample sizes before declaring a winner.
Read the original → docs.cloud.google.com
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