A deployed model based on a genetic algorithm may take several minutes to produce a prediction and must process requests that include up to 100 MB of data. Which deployment option minimizes operational overhead while meeting these requirements?

Amazon SageMaker Asynchronous Inference is the best option because it is designed for models with large payload sizes (up to 1 GB) and long processing times (up to 1 hour), directly addressing the problem's requirements of 100 MB data and several minutes for prediction. It also minimizes operational overhead by managing the underlying infrastructure. Amazon EC2 instances in an Auto Scaling group with an Application Load Balancer would require significant operational overhead for managing the instances, scaling, and load balancing, which is not ideal for minimizing overhead. Amazon SageMaker real-time endpoints are optimized for low-latency, high-throughput predictions, typically with smaller payloads and faster response times, making them less suitable for the given constraints of large data and long prediction times. Deploying to Amazon ECS on Amazon EC2 instances would also involve substantial operational overhead for container orchestration, scaling, and infrastructure management, similar to the direct EC2 approach.