A machine learning team has a fraud detection workflow that requires three sequential processing steps on every real-time request: a scikit-learn preprocessing container that normalizes and encodes features, an XGBoost model container that produces a score, and a lightweight post-processing container that applies business thresholds and formats the output. All three must run on the same request with minimal added network latency, and the team wants to deploy them behind a single HTTPS endpoint without managing separate services or custom orchestration code. Which SageMaker deployment approach best satisfies these requirements?