tensorflow in production
I have an answer now for my question.
I will share briefly the main steps / technologies I used to deploy the model in production.

I am using Python programming language.
After training and generating valid models I wrote a restful api using Python programming language and flask.

Using flask you can write a restful api. Three important points:

1- It is very important to give attention to where you will define the model architecture/initialize the parameters/ define the session.
Avoid doing this each time you call the restful api. this will be very expensive.

2- Flask provide a very good mechanism to run servers in production environment.
Read about flask + wcgi 
Avoid runing the server code (the resful api) directly, in this case you will not have direct and full control. 

3- Watch the memory and the cpu usage, make sure to limit the maximum number of instances that can run in parallel. Remember these models can take a lot from the memory.

unfortunately, I can not share codes with public. But hopefully my answer can give an idea about how to do it in production.