Model Serving With Request Batching Auto Scaling And Multi Model Composition

via “model serving with request batching and dynamic scaling”

Distributed AI framework — Ray Train, Serve, Data, Tune for scaling ML workloads.

Unique: Implements request batching at the actor level (not at HTTP gateway) by buffering requests and forwarding them as batches to model inference, reducing per-request overhead. Supports composition via deployment graphs where outputs of one deployment feed into another, enabling complex serving topologies without external orchestration.

vs others: More efficient batching than FastAPI + Gunicorn due to actor-level buffering; simpler than Kubernetes + KServe for multi-model serving; tighter integration with Ray Train for serving trained models without export.