Continuously retraining the model using a streaming data pipeline (C) ensures accuracy and relevance for real- time fraud detection. Financial fraud patterns evolve rapidly, requiring the model to adapt to new data incrementally. A streaming pipeline (e.g., using NVIDIA RAPIDS with Apache Kafka) processes incoming transactions in real time, updating the model via online learning or frequent retraining on GPU clusters. This maintains performance without downtime, critical for production environments.
* Static dataset retraining(A) lags behind emerging patterns, reducing relevance.
* Retrain only on accuracy drop(B) is reactive, risking missed fraud during degradation.
* Parallel rule-based systems(D) add redundancy but don't improve model adaptability.
NVIDIA's AI deployment strategies support continuous learning pipelines (C).