A system saves new documents to S3 every 3 seconds. Three SageMaker model versions exist to classify each document, and the company wants to deploy all three to classify each incoming document while keeping operational overhead minimal. Which deployment approach meets this requirement with the least management effort?

Deploying all three models as production variants on a single SageMaker endpoint is the most efficient solution. This approach allows a single endpoint to host multiple models, reducing operational overhead compared to managing separate endpoints. The S3 event triggering a Lambda function ensures real-time processing as documents arrive. The Lambda function can then invoke the single SageMaker endpoint, specifying each production variant to get classifications from all three models. The other options are less efficient: Starting three batch transform jobs for each document introduces significant latency and management complexity, as batch transform is designed for larger, asynchronous workloads, not real-time, per-document inference. Deploying each model to its own SageMaker endpoint increases management overhead and cost due to maintaining multiple endpoints. Creating three separate Lambda functions and S3 event notifications further increases complexity and management burden compared to a single Lambda and endpoint.