Power BI Tips #2 – HOW TO DEPLOY ML MODEL WITH AZURE ML STUDIO, PYCARET AND POWER BI SERVICE AND HOW TO CONSUME IN POWER BI REPORT

In this article we will see:

1. How to deploy a simple classification model on Azure Machine Learning Studio using PyCaret and how to use it in a Power BI report.
2. How to implement automated machine learning in Azure Machine Learning Studio and how to use it in a Power BI report.
3. How to create an AutoML model with PowerBI Service dataflow and how to use it in a Power BI report.

The dataset used in this demonstration is the Diabetes Health Indicators Dataset.
The dataset was randomly split into:

• 60% for model training (about 152k rows)
• 2% as hold-out sample (about 5k rows)
• 38% as unseen data (about 96k unlabeled rows)

You can find original dataset here and my repo on GitHub here!

 

Working with PyCaret in Azure ML Studio

This is a step by step tutorial on how write, train and deploy ML model using PyCaret in Azure Machine Learning Studio

How to use in Power BI desktop:

You can download pbix file from here and see python visual script to plot ml metrics

 

Workign with Azure AutoML feature

You can download pbix file from here and see python visual script to plot ml metrics.

 

Workign with Dataflow

Explore Prediction report:

You can download pbix file from here

 

Comparison

take care!!