Model deployment engine architecture receives inputs from three critical sources: a control plane for orchestration directives, a supplier interface for model artifacts and dependencies, and a model interface for inference specifications. Data flows from each input stream converge at the deployment engine, which coordinates artifact staging, validation, and runtime provisioning. This multi-input pattern decouples model lifecycle management from infrastructure control, enabling teams to scale model serving independently. Fork and customize this diagram on Diagrams.so to match your ML platform's deployment topology, whether using Kubernetes, Seldon, or custom orchestrators.