TensorFlow Serving is a part of TensorFlow Extended(TFX) that makes deploying your machine learning model to a server more comfortable than ever. Before Google released TensorFlow Serving, your model has to be deployed into production using Docker. Using Docker to deploy your model is tedious, time-consuming, and prone to many errors. TensorFlow Serving provides us with an API that can be called upon using HTTP requests to run inference on the server. In this blog, we will serve an emotion recognition model and, through that, understand the basics of TensorFlow Serving.
Why serve a model?
Once you have trained your model, it has to be deployed into production so that it can be used. Various methods can be used to deploy the model like deploying locally on phones using TFlite, deploying on a website using TFjs, creating a docker container to deploy your model on the cloud, etc. TensorFlow Serving has an advantage over the other methods for the following reasons.
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