14. Endpoints & REST APIs

Production Environment and the Endpoint

When we discussed the production environment , the endpoint was defined as the interface to the model. This interface ( endpoint ) facilitates an ease of communication between the model and the application . Specifically, this interface ( endpoint )

  • Allows the application to send user data to the model and
  • Receives predictions back from the model based upon that user data .

Model, Application, and Endpoint

One way to think of the endpoint that acts as this interface , is to think of a Python program where:

  • the endpoint itself is like a function call
  • the function itself would be the model and
  • the Python program is the application .

The image above depicts the association between a Python program and the endpoint , model , and application .

  • the endpoint : line 8 function call to ml_model
  • the model : beginning on line 14 function definition for ml_model
  • the application : Python program web_app.py

Using this example above notice the following:

  • Similar to a function call the endpoint accepts user data as the input and returns the model’s prediction based upon this input through the endpoint .

  • In the example, the user data is the input argument and the prediction is the returned value from the function call .

  • The application , here the python program , displays the model’s prediction to the application user .

This example highlights how the endpoint itself is just the interface between the model and the application ; where this interface enables users to get predictions from the deployed model based on their user data .

Next we'll focus on how the endpoint ( interface ) facilitates communication between application and model .

Endpoint and REST API

Communication between the application and the model is done through the endpoint ( interface ), where the endpoint is an Application Programming Interface ( API ).

  • An easy way to think of an API , is as a set of rules that enable programs, here the application and the model , to communicate with each other.

  • In this case, our API uses a RE presentational S tate T ransfer, REST , architecture that provides a framework for the set of rules and constraints that must be adhered to for communication between programs.

  • This REST API is one that uses HTTP requests and responses to enable communication between the application and the model through the endpoint ( interface ).

  • Noting that both the HTTP request and HTTP response are communications sent between the application and model .

The HTTP request that’s sent from your application to your model is composed of four parts:

  • Endpoint
    • This endpoint will be in the form of a URL, Uniform Resource Locator, which is commonly known as a web address.
  • HTTP Method
    • Below you will find four of the HTTP methods , but for purposes of deployment our application will use the POST method only .
  • HTTP Headers
    • The headers will contain additional information, like the format of the data within the message, that’s passed to the receiving program.
  • Message (Data or Body)
    • The final part is the message (data or body); for deployment will contain the user’s data which is input into the model .

The HTTP response sent from your model to your application is composed of three parts:

  • HTTP Status Code
    • If the model successfully received and processed the user’s data that was sent in the message , the status code should start with a 2 , like 200 .
  • HTTP Headers
    • The headers will contain additional information, like the format of the data within the message , that’s passed to the receiving program.
  • Message (Data or Body)
    • What’s returned as the data within the message is the prediction that’s provided by the model .

This prediction is then presented to the application user through the application . The endpoint is the interface that enables communication between the application and the model using a REST API .

As we learn more about REST*ful *API , realize that it's the application’s responsibility:

  • To format the user’s data in a way that can be easily put into the HTTP request message and used by the model .
  • To translate the predictions from the HTTP response message in a way that’s easy for the application user’s to understand.

Notice the following regarding the information included in the HTTP messages sent between application and model :

  • Often user's data will need to be in a CSV or JSON format with a specific ordering of the data that's dependent upon the model used.
  • Often predictions will be returned in CSV or JSON format with a specific ordering of the returned predictions dependent upon the model used.

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