TensorFlow Serving

TensorFlow Serving is a system for serving trained TensorFlow models in production. Models are exported in SavedModel format, which includes graph assets and signatures describing supported input and output tensors. The serving process loads a model from a repository path and exposes prediction operations through interfaces such as REST or gRPC. Clients must follow the exported signature; mismatched tensor names, dtypes or shapes can cause request failures or incorrect preprocessing. Versioned model directories allow the server to observe and load model versions. A version policy determines which versions are available, such as serving the latest or selected versions depending on configuration. Version management does not itself validate a candidate. Teams should test a new SavedModel against representative requests, compare outputs with expected tolerances and connect deployment to external promotion gates. Batching can combine requests to improve hardware utilization and throughput. It also adds queuing delay and memory use, so batch size and wait time should be tuned against latency objectives. Resource configuration, model warmup and process health affect startup and readiness. TensorFlow Serving can run in containers or orchestration platforms, but network security, authentication, autoscaling and observability need deployment design. A serving path includes more than the model binary: feature transformation, request serialization, model signatures, version selection and response interpretation all matter. Preserve compatibility or version the API when signatures change. Monitor request latency, error rates, model versions and delayed quality metrics. A SavedModel loading successfully does not prove that the model is accurate on live inputs. TensorFlow Serving provides a model-serving runtime; evaluation, traffic control and incident response remain responsibilities of the surrounding system.