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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

The Power of Azure ML and Power BI: Dataflows and Model Deployment

Kaushikrch
Kaushikrch Last Updated : 23 Nov, 2020
5 min read

Overview

  • Learn about the integration capabilities of Power BI with Azure Machine Learning (ML)
  • Understand how to deploy machine learning models in a production environment

 

Introduction

It’s not news anymore that Machine Learning (ML) models have excellent predictive capabilities. However,

With more power comes more responsibilities.

Although data scientists train a considerable amount of data to come up with predictions for the future, it is a challenge to present the results comprehensively. Moreover, it is a significant challenge to deploy the models in a production environment. While Azure helps you build powerful ML models without any coding, Power BI offers capabilities to incorporate insights from an ML model in BI reports.

In previous posts(links given at the end of the article), we have explored how Power BI can help businesses analyze data and how to create reports and dashboards in an undemanding manner. In this post, we will understand how to use Power BI and Azure ML to deploy results from an ML model.

This post assumes readers have a basic knowledge of ML models. To understand how ML models can be deployed in a production environment using Flask, you can read this fantastic introductory text:

 

Table of Contents

  1. What is Azure ML?
  2. Peep into the Azure ML Studio (Classic)
  3. Building and deploying the model as a web service
  4. Deploying the ML model from Azure ML platform to Power BI
  5. Visualizing the results to create actionable business insights

 

What is Azure ML?

Data scientists are well aware of the complex and gruesome pipeline of machine learning models. Firstly, solving a business problem starts with the formulation of the problem statement. Secondly, the team generates specific hypotheses to list down all possible variables affecting the objective. Then, data is extracted or collected from relevant sources.

In the next step, we perform data experimentation as a step-by-step process to derive insights on missing values, data distribution, and analysis commonly referred to as exploratory data analysis (EDA).  Fifth, we develop a set of predictive models with the evaluation of a relevant error metric for a train-test split using a preferred validation technique.

Further, these models are tuned using hyperparameter to arrive at the optimal results. Finally, we select the best performing model for deployment with real-time scoring or batch scoring.

This task requires writing lines of codes in software like R or Python. However, Azure ML helps simplify this complex process of building predictive models.

 

Peep into the Azure ML Studio (Classic)

Azure ML Studio (classic) allows you to build, train, optimize, and deploy ML models using a GUI. It is a no-code environment. Therefore, you can create complex ML models for clustering, regression, classification, anomaly detection, and recommendation models as well as data, statistical, and text analysis.

Azure ML Studio (Classic)

You can build your ML models with a functional connection of graphs. Thus, even a beginner with just a theoretical background in machine learning can easily use it. Multiple tutorials are provided for the untrained. For instance, the screenshot below shows the comparison of two-class classification models to predict whether the income of an adult would be above or below $50,000 using socio-demographic variables as the features.

Azure ML Studio (Classic)

 

Building and deploying the model as a web service

Once the workflow of building an ML model is ready, the model is trained with a click of a button (RUN). As the models run, the virtual environment and parallel computing make the training time faster.

The screenshot below shows the results error metrics of cross-validation across various folds.

Cross Validation

Finally, after running the Predictive Experiment (training the model) from the sample, we deploy the model as a web-service using the “Deploy Web Service” option.

 

Deploying the ML model from Azure ML platform to Power BI

Deploying and operationalizing the ML model is the next step in the pipeline. In this step, test the model on live data in production. The live data can lie inside a Power BI environment. Power BI is an on-premise service using which business intelligence functions are performed. Thus, a report or a dashboard displaying the current business scenario along with real-time predictions from an ML model can add significant value to organizations.

Suppose, a company wants to score the income of individuals based on their socio-demographic characteristics. To perform this task, we have to add the entity we want to score (predict the probability of earning more or less than $50,000.

Clicking on the Azure Machine Learning button under the AI insights tab of the PowerQuery editor connects to the Azure ML platform with the same account.

Azure ML Toolbar

Power BI will list down all the Azure ML web-services that you can access. Select the appropriate web-service; the UI will map all the columns of your entity to the inputs of the web-service by matching their names and types. Click “Apply” to see a preview of the scoring. Thus, the scored probabilities show up as a new column in the table.

 

Visualizing the results to create actionable business insights

Till now, we have applied the ML model on live data loaded in the Power BI environment. You can use the predicted probabilities from the Azure ML model to refresh the business report/dashboard displayed in Power BI.

 

Power BI Visualization

Source: Power BI Community Blog, Azure Machine Learning in Power BI Dataflows, retrieved from https://community.powerbi.com/t5/Community-Blog/Azure-Machine-Learning-in-Power-BI-Dataflows/ba-p/709744#

 

In other words, it gives the decision-makers a view into not only the historical context of the business problem but also how does the business evolves in the future using predictive modeling.

 

End Notes

In this article, we learned about the training and deployment of models in the Azure ML platform. We also understood how the predictive capabilities of Azure ML could be leveraged to generate insights into the future in a live environment.

To conclude, this integrated environment gives more power into the hands of data scientists, business intelligence professionals, and strategic decision-makers. They can quickly capitalize on the beneficial aspects of these integrated environments so that a business problem can be solved through collaboration and not in a silo.

Further, to learn about the capabilities of Power BI, you can read these posts:

I hope this article was valuable. Reach out to us if you have any queries in the comment section below.

AzureMLEDAModel deployment
Kaushikrch
Kaushikrch

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Responses From Readers

Clear

Ryan
Ryan

Hi, i tried following the steps above but somehow my Power BI desktop said it cannot find any machine learning model. Do I need to have Azure subscription and paid workspace to deploy Azure ML in Power BI? Thanks

123

Kaushik
Kaushik

Hi Ryan - Are you using the same account for Microsoft Azure as for Power BI? Also, are you logged in to Power BI when trying to implement this step? You can check the top-right corner of Power BI Desktop. I used the free account for Microsoft to perform this exercise. Regards, Kaushik

123

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