4 docs tagged with "deployment"

Every other page in this folder covers one piece of the deployment problem in depth — a device family, a runtime, a compiler, a quantization technique. This page is the one that ties them together into an order of operations: which decisions to make first, what to check before committing to a target, and what to verify before calling a deployment done. It is deliberately a procedure rather than a survey — the pages it links to already carry the depth, and repeating that depth here would only get it out of sync with the pages that own it.

The model scores 95% accuracy in the notebook — and returns nonsense in production. This is one of the most common, most preventable failure modes in applied vision, and it's almost never the model's fault: it's a mismatch between how the training pipeline preprocessed images and how the serving pipeline does.

Hand-rolling a FastAPI app around a chain or graph gives you control LangServe doesn't: custom auth, arbitrary request/response shapes, and full ownership of streaming.

langserve wraps a Runnable in a FastAPI app and gives you /invoke, /batch, /stream, and /stream_events endpoints, plus request/response schemas derived from the chain's input/output types — for free, with no route code of your own.