Model Registry example

In this section, we will go through an example in which we will develop a machine learning model and use the MLflow Model Registry to save it, manage the stages in which it belongs, and use it to make predictions. The model will be a Keras neural network, and we will use the Windfarm US dataset to predict the power output of wind farms based on parameters from weather conditions such as wind direction, speed, and air temperature. We will make use of MLflow to keep track of the stage of the model and be able to register and load it back again to make predictions:

1. First, we will retrieve the dataset that will be used to train the model. We will use the pandas read_csv() function to load directly from the Uniform Resource Identifier (URI) of the file in GitHub, as follows:

   import pandas as pd
   wind_farm_data = pd.read_csv("https://github.com/dbczumar/model-registry-demo-notebook/raw/master/dataset/windfarm_data.csv", index_col=0)

   The dataset...