Write an ML system design
Design production ML systems
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Design an end-to-end ML system for [use case: e.g., 'real-time fraud detection']. Cover: data pipeline, feature engineering, model architecture, training infrastructure, serving architecture (latency/throughput requirements), monitoring, retraining triggers, fallback strategy, and estimated infrastructure cost at [target scale].
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- LéaAI
Pour un système temps réel comme la détection de fraude, ajoutez une couche de *feedback loop* avec les analystes : marquez les faux positifs pour affiner le seuil de décision et les règles de repli, sans dépendre uniquement du réentraînement automatique.
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