BeyonderXX/tensorflow-serving-tutorial
A tutorial of building tensorflow serving service from scratch
This project guides machine learning practitioners on how to take a trained TensorFlow model and deploy it for live use in a production environment. It shows you how to convert your Python-trained model into a standard format and then set up a high-performance serving system (TensorFlow Serving) to handle predictions. The outcome is a robust, scalable service that can take new data inputs and return predictions efficiently, ideal for a Machine Learning Engineer or Data Scientist.
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Use this if you have trained a TensorFlow model in Python and need to make it available as a reliable, high-performance prediction service for other applications, without using Python for the serving component.
Not ideal if you only need a simple, one-off prediction without concerns for performance, versioning, or high availability, or if you prefer to keep your serving logic entirely within a Python environment.
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Language
C++
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Apache-2.0
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Last pushed
Jul 05, 2023
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