The primary requirement is minimal infrastructure management for a real-time model with moderate, potentially intermittent traffic (up to 50 concurrent requests). Amazon SageMaker Serverless Inference is specifically designed for this use case. It automatically provisions, scales, and manages the underlying compute resources, completely abstracting away server management. The user pays only for the compute time used to process inference requests, making it the solution with the least operational overhead while meeting the real-time performance needs for this scale.

A. A standard SageMaker real-time endpoint requires you to provision and manage specific ML instance types and configure scaling policies, which incurs more operational overhead than a serverless option.

B. An asynchronous SageMaker endpoint is designed for large payloads and long-running inference jobs (up to 15 minutes), not for the low-latency responses required by a real-time fraud detection system.

C. Deploying on EC2 with an Auto Scaling group and a load balancer represents the highest operational overhead, as it requires managing the OS, runtime, scaling, and all underlying infrastructure components.

1. Amazon SageMaker Developer Guide, "Serverless Inference": "With SageMaker Serverless Inference, you can quickly deploy your machine learning models for inference without having to configure and manage the underlying infrastructure... Serverless Inference is ideal for workloads that have intermittent or unpredictable traffic patterns." This directly supports the choice of a serverless endpoint for minimal management.

2. Amazon SageMaker Developer Guide, "Real-time inference": This section details the process of creating a real-time endpoint, which includes "Specify the number and type of ML compute instances to deploy your model to." This confirms the management overhead associated with provisioned endpoints, making option A less suitable.

3. Amazon SageMaker Developer Guide, "Asynchronous Inference": "This option is ideal for requests with large payload sizes... and long processing times... It is not intended for low-latency (sub-second) inference requests." This confirms that asynchronous inference is inappropriate for the real-time requirement.

4. AWS Whitepaper, "Machine Learning Best Practices on AWS", Page 23: The paper discusses hosting options and highlights that "For workloads with infrequent or intermittent traffic patterns... AWS offers Amazon SageMaker Serverless Inference... This option removes the need to select instance types or manage scaling policies." This reinforces that serverless is the best choice for minimizing operational overhead in this scenario.