You are tasked with deploying a machine learning model into a production environment for real-time fraud detection in financial transactions. The…

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).

Reference:NVIDIA RAPIDS documentation; Real-Time AI Deployment Guide on nvidia.com.