TensorFlow Production

TensorFlow Production involves deploying trained models to production systems that serve predictions at scale. It includes model serving infrastructure (TensorFlow Serving), ML pipelines (TensorFlow Extended), model management, versioning, A/B testing, and monitoring. Production TF requires reliability, latency guarantees, and safety (no model crashes affecting users). TFServing is Google's high-performance inference server optimized for TensorFlow models. It handles batching, version management, and canary deployments. TFX is a pipeline framework orchestrating the full ML lifecycle: data validation, training, model evaluation, and automated deployment.