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TensorFlow Serving deploys ML models for production inference with high performance. Models are versioned automatically for A/B and rollback. Export models in SavedModel format using tf.saved_model.save. Deployment can be via Docker containers or bare metal installation. REST API provides predict, classify, and regress endpoints. gRPC API offers better performance for high-throughput scenarios. Configuration files specify model versions and base paths. Monitoring with Prometheus metrics tracks request latency and throughput. Batching requests improves throughput for GPU inference. Dynamic batching groups concurrent requests automatically. Models can be hot-loaded without server restart. Version policy controls default serving version. Signature definitions specify input and output tensor mappings. Warmup requests initialize model states before production traffic. Resource quotas prevent models from consuming too many resources. TensorFlow Serving can manage multiple models simultaneously. Container orchestration with Kubernetes enables automatic scaling. Model optimization with TensorRT improves inference performance. TensorFlow Extended (TFX) provides end-to-end ML pipeline management.