If you're only doing .fit() and .evaluate(), you're probably wasting your time.
Deployment is the essential part of the whole Machine Learning pipeline which leads the models to complete their goals.
In this video, we'll focus on deploying models using Tensorflow Serving which is a highly optimized tool from official Tensorflow used to deploy and manage multiple models at the same time and supports both CPU and GPU deployments. The best is you don't have to worry about any type of codebase, it manages everything itself.
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