Implementing online model serving

In this section, we will train a dummy SGDRegressor model, use Flask to create a server and API for the online prediction endpoint, use Postman to send a request to the server, and update the model with the input data and the prediction made by the last model.

For the end-to-end example we are going to run, you need to import the following modules:

from flask import Flask, request
import numpy as np
import json
from sklearn.linear_model import SGDRegressor

Let’s begin:

1. First of all, let’s create a model with some dummy data in the following code snippet:

   X = [

       [1, 1, 1],

       [1, 1, 1],

       [1, 1, 1],

       [2, 2, 2],

       [2, 2, 2],

       [2, 2, 2]

        ]

   y = [1, 1, 1, 2, 2, 2]

   model = SGDRegressor()

   model.fit(X, y)

   print("Initial coefficients")

   print(model.coef_)

   print("Initial...