Model Deployment
Definition pending
We've cataloged "Model Deployment" but haven't written a full definition yet. Definitions are hand-curated rather than auto-generated, so it takes time to cover every term.
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Practical example
Model deployment puts a trained model into production — serving predictions to real users. It's more than just loading weights: you need scaling, monitoring, versioning, rollback, and A/B testing. For LLMs, deployment frameworks (vLLM, TensorRT-LLM, Ollama) handle the heavy lifting.
Workflow example
Model deployment workflow: (1) export model to production format: GGUF (llama.cpp), ONNX (CPU), TensorRT (GPU), (2) containerize: Docker image with model + inference server, (3) deploy: K8s pod, dedicated VM, or edge device, (4) configure: concurrency, batching, memory limits, max context length, (5) test: run evaluation on deployed model (not just local), (6) monitor: latency, throughput, error rate, VRAM usage — alert on anomalies.