What is the method for deploying a TensorFlow model online?

There are several main methods for deploying TensorFlow models.

1. TensorFlow Serving is a standalone model server that can be deployed in production environments. It allows you to deploy trained TensorFlow models and provides both RESTful API and gRPC interfaces for clients to perform inferences via network requests.
2. TensorFlow Lite is a lightweight version of TensorFlow designed for mobile and embedded devices. It allows TensorFlow models to be converted into a format suitable for mobile devices and run locally on the device.
3. TensorFlow.js is a library that allows TensorFlow models to run in browsers and Node.js, enabling model deployment through JavaScript.
4. Integration of deep learning frameworks: Some cloud service providers offer services that integrate TensorFlow models, allowing users to deploy models to the cloud for inference.

Here are some common deployment methods for TensorFlow models, choose the one that best suits your needs for deployment.