Tomaz Kastrun continues an advent of Azure ML. Day 16 shows off MLflow:
Yesterday we have looked into how to start the MLflow configurations and today, let’s put this to the test.
We will create a new notebook and use Heart dataset (link to dataset) to toy around. We will also import xgboost classifier to asses the accuracy of the presence of heart disease in the patient. We will be using a categorical (integer) variable with values from 0 (no presence) to 4 (strong presence) and attempt to classify based on 15+ attributes (out of more than 70 attributes).
Day 17 pivots to using the responsible AI dashboard:
Azure ML has provided users with collection of model and data exploration with the Studio user interface. But it also provides compatible solutions with Azure ML and Python package responsibleai. With the help of widgets, we will create an sample of dashboard to explore the solution with assessing the responsible decisions and actions.