TensorFlow Serving is a powerful tool for deploying TensorFlow models in real-world applications. It simplifies the process of serving models over a network, offering features like model versioning, monitoring, and scalability. To get started, you need to install TensorFlow Serving on your server. Once installed, you can export your trained TensorFlow model in the SavedModel format, which includes all the necessary information for serving. Then, you can start TensorFlow Serving with your exported model and specify details like the model name, version, and port.

Once TensorFlow Serving is up and running, you can send inference requests to it using HTTP or gRPC protocols. The model name and version are specified in the request, allowing for easy access to different versions of the model. TensorFlow Serving supports model versioning, which means you can deploy multiple versions of a model simultaneously and switch between them seamlessly. This is useful for tasks like A/B testing and gradual rollouts of model updates.

TensorFlow Serving also provides monitoring and management capabilities through a web interface, allowing you to monitor metrics like model performance, request latency, and throughput in real-time. It's designed for scalability and high performance, supporting concurrent serving of multiple models, load balancing, and distributed deployment.

Overall, TensorFlow Serving streamlines the deployment of TensorFlow models in production environments, making it easier to manage and serve machine learning models for real-world applications. By incorporating TensorFlow Serving into your deployment workflow, you can ensure efficient and reliable serving of models to meet the demands of your applications.

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