As mentioned in the tensorflow documentation TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments while keeping the same server architecture and APIs. TensorFlow Serving provides out-of-the-box integration with TensorFlow models but can be easily extended to serve other types of models and data.
Eager execution in TensorFlow allows for rapid prototyping. The eager execution follows the imperative programming paradigm, where any operations can be performed immediately, without having to create a graph.
TensorBoard is TensorFlow's visualization tool used to visualize a computational graph. It can also be used to plot various quantitative metrics and the results of several intermediate calculations.
The feed_dict parameter is the dictionary where the key represents the name of the placeholder and the value represents the value of the placeholder.
We use the variables for storing the values. Variables are used as input to several other operations in a computational graph.
We can think of placeholders as variables, where we only define the type and dimension, but do not assign the value. Values for the placeholders will be fed at runtime.