A real-time fraud detection model currently uses SageMaker Asynchronous Inference but consumers are experiencing delays. You must improve inference latency and also generate alerts when model quality degrades. Which solution meets these requirements?

The correct solution is to switch to SageMaker real-time inference and use SageMaker Model Monitor. Real-time inference is designed for low-latency, high-throughput use cases like fraud detection, where immediate responses are crucial. SageMaker Model Monitor continuously analyzes model predictions and features in production, automatically detecting data drift, model quality degradation, and bias, then sending alerts. Using SageMaker batch transform would increase latency, as it's designed for large datasets processed asynchronously, not real-time responses. SageMaker Serverless Inference offers low latency but SageMaker Inference Recommender is for optimizing instance types and endpoints, not for ongoing model quality monitoring and alerting. Continuing with SageMaker Asynchronous Inference would not resolve the latency issue, and Inference Recommender does not provide model quality notifications.