Comparing Azure ML Lab and PowerBI , like apples and oranges

1. Presenting Sponsor
Azure Machine Learning Studio and
PowerBI
Yana Berkovich, Microsoft MVP, BI & Applications Lead , Onni Group of Companies
Moderated By: Aasish Sharma

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5. Yana Berkovich
Microsoft Data Platform MVP
VANO365 User group president
Vancouver PowerBI user group member and speaker
SQL and O365 Saturdays and community events speaker
@Yana_Berkovich
https://www.linkedin.com/in/
yanaberkovich/
YanaBerkovich.com
Yana@yanaberkovich.
com

6. Presenting Sponsor
Azure Machine Learning Studio and
PowerBI
Yana Berkovich, Microsoft MVP, BI & Applications Lead , Onni Group of Companies

7. Azure Machine Learning Lab &
PowerBI
Why are we using those tools?
AzureML Lab vs PowerBI
Getting the Data
PreProcessing the Data
The prediction model
Sample, population and your data set
Example - Exponential Smoothing Method
Quick Summary – what to use when…?

8. Data science as Gartner defines it…

9. ML is in the Peak since 2015! Gartner

10. Decision Support​
This is how it looks like This is how the Data Analysis field
is trying to make it look like:
James Taylor, leader of Decision Management Solutions
http://decisionmanagementsolutions.com/
Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics (IBM
Press)

11. What is Azure ML Lab/Studio?
Built on top of the machine learning capabilities of several
Microsoft products and services.
Shares many of the real-time predictive analytics of the new
personal assistant - Cortana. Azure ML also uses proven
solutions from Xbox and Bing.
Components
Lab
Gallery
Only cloud based tool in Azure
https://studio.azureml.net/Home
Audience: Data Analysts, Statisticians,
Actuary, Data Scientists …
Users: Data Analysts, Data Scientists

12. A suite of business analytics tools that deliver insights.
Connects to hundreds of data sources, simplifies data prep, and drives
ad hoc analysis. Produce beautiful reports, then publish them for your
organization to consume on the web and across mobile devices.
Scalable across the enterprise, with governance and security built-in.
Components
Desktop
O365
Mobile
Embedded
Report Server
Insights apps
Cloud solution, on-premise solution, mobile solution
https://powerbi.microsoft.com
What is PowerBI?
Audience: Business Users & Managers
Users: IT, Finance, Marketing, Manufacturing,
Data Analysts…

13. Azure ML
A service that was created for developers and
data scientist
Business users, end users and customers,
Analysts friendly
Predict the future
Train and create custom models based on
statistics that will help answer questions
Visualize the existing data for business use
Answer business questions
Predict the future??!! Is there a better why that
can potentially generate more value for the
business?
PowerBI
Get insights to give information for the Decision
Support
Who? What? Why?

14. Some useful sources for the beginners…
Machine Learning
• https://www.kaggle.com/
• https://blogs.technet.microsoft.com/machinelear
ning/
• https://azure.microsoft.com/en-
us/blog/tag/azure-machine-learning/
• http://radacad.com
• https://www.sqlbi.com/author/marco-russo/
• https://academy.microsoft.com/en-
us/professional-program/tracks/data-science/
https://twitter.com/kdnuggets/status/992478222695780352
PowerBI
• http://radacad.com
• https://www.sqlbi.com/author/marco-russo/
• https://www.excelguru.ca/blog/
• https://powerbi.microsoft.com/en-us/blog/

15. Getting the data

16. Getting the Data - Decision Support
System and the tip of the iceberg
Predict
Model
Insights
Information
Data
Data Science starts with data gathering,
Getting the data from the metrics is hard!
The data collection process can be a result
of meetings, Telemetry, IOT…

17. How are we using ML Studio? Machine Learning Process Cycle
Adapted from:
Azure Machine Learning Studio Four Tips
from the Pros by
Brad Llewellyn’s presentation
PASS Link
https://www.youtube.com/watch?v=lRxZkBubbd
4&feature=youtu.be
Data
Collection
Data
Cleansing
Data
Manipulations
Model
Creation
Model
Evaluation

18. IT all starts with the right question and Business Goal!

19. Case Study
Airplanes are never late….
We are going to analyze the data set of
flights during the month of October
This data set was taken from the
sample data sets in ML studio

20. Getting the Data
Azure ML Lab PowerBI
Data set CSV file, txt, Excel, Hive table, SQL table,
Odata, SVMlight, Zip, R object
Source – CSV file in this case,
More than a 100 different sources
Source Type
Data Delimiter
Data connection and refresh

21. Visualizing the Data
Azure ML Lab PowerBI
Data Preview
Histograms, box plots
Raw data
This is the main goal of this tool – Data visualization
Recently, similar automatic visualizations
Data view for all the visualizations click the
Aggregated data

22. Visualizing the Data PowerBI
Quick insights mode
Quick insights mode

23. PreProcessing the data

24. Azure ML
Data Type, Change metadata module Data Type – automatic detection, Change the
type in a SQL query, directly on the column
Clean missing data – minimum maximum missing
value ration (even 100% of the data cleaned)
Clean duplications, first last top rows
Use DAX queries and R
PowerBI
Create measures calculated based on data ranges
Data Cleansing
Convert the data into categories from range
Group categorical values
Edit metadata
SMOTE - increasing rows/facts number
Edit metadata

25. Azure ML
Selecting columns, Selecting columns,
Merging, Join with other data source – SQL
manipulations, R Manipulations, Python
manipulations
Building Dimensions – Time dimension, Airport
Dimension…
Creating custom measures, quick measures and
code based measures using DAX
PowerBI
ERD- create connections between the dimensions
and the fact tables
Data Manipulations
Creating Join through SQL query, Merging,
Appending lines
Creating EDR through join of another dimension
table for the selected columns
Using R or Python for creating custom measures
(avg, mean…)

26. Azure ML
Only if you build a model for that
Out of the box visualization for the data set with
2 graphic options as previously mentioned
Q&A functionality recently available on desktop
Looks very similar to the visualizations that exist
in ML lab
Enables the user to add the FAQ visualization to
the dashboard or report
“native” language questions answered-
What is the most late flight from Chicago airport?
PowerBI
Data Manipulations

27. The prediction model

28. Main Steps in creating an Experiment / Report
AzureML Experiment PowerBI Report
 Get data
 Clean the data
 Prepare the data (adding columns, calculations,
missing data types, joins, SQL manipulations…)
 Divide the data – sample for the model to train,
data for evaluation
 Choose the model
 Train the Algorithm
 Score using the data for evaluation
 Evaluate
 Save as a trained model for later use or
 Create Web Service and predict for new data sets
 Get or connect to the data
 Clean the query
 Create measures and dimensions
 Create connections using ERD
 Create data visualizations
 Q&A Analyze the data and get the answers to
your question
 Add visualizations to Dashboard
 Create Application and publish

29. Which Questions do we ask our Model?
Azure ML Lab PowerBI
 How do we predict if a certain flight is going
to be late?
 How does the weather affect the flight being
late?
 If we are going to fly from a certain airport,
will our flight be late – Ask the Web service!
 What is the chance for the flight to be less
than 15min late if it’s AA? What is the
precision of this prediction?
Future Events
 We generally don’t! It is mostly a data
Visualization tool not a tool we use to predict
 What is the average? Max? Min?
 Which Airport has the most late arrivals?
 What is the correlation and the trend between
the weather and the delay time?
 Clustering the data, which airports are in the
most late cluster? – histograms and brick
charts
Events that have already happened, limited
prediction

30. What is a prediction model?
Which Algorithm is the best fit to predict the results, depending on the data
Has the data seasonal? hads repetitions? Categorical?
Linear Regression or Poisson Regression?
How can we know what works best? Based on the past results!
Main model types:
Anomaly Detection
Classification
Clustering
Regression

31. Statistics…and prediction models How do we predict the average
late departure?
Average
Single Exponential Smoothing
Exponential smoothing is a rule of thumb
technique for smoothing time series data using
the exponentialwindow function. Whereas in the
simple moving average the past observations are
weighted equally,exponential functions are used
to assignexponentially decreasing weights over
time.
( Wikipedia to the rescue… )
Moving Average
The last month might be a better prediction for
flights than the last 20 months
Weighted Moving Average
Some observations are more significant than others,
flights of a domastic flight company have different
performance and cannot be compared to others or
big vs small planes
Can be chosen, for the single smoothing, between 0.1 and
0.9, is chosen through a local optimal minimum value
We choose the best value for α so the value which results
in the smallest MSE. (Mean of Square Errors)

32. Adding information to our data visualization
PowerBI
Min value line
Max value line
Trend line – we can see that the AVG delay time
increases?
Expediential Smooth
Seasonality – 7 points (week in a month)
Ignore last 10 points – to check our prediction
Forecast length- to see what the other 7 days will
look like

33. Adding information to our data visualization
PowerBI – How can we explain the predicted results?
Trend line – we can see that the AVG delay time
increases?
How can we validate and score the predicted results? Azure ML Lab
• End of October - Thanksgiving?
• Weather changes at the airports for the worse
• The trend line doesn’t continue for the predicted data
• How can we control the Alpha? Well in Power View for O365, not in PowerBI yet

34. More options in PowerBI? – R
R model for more, simple prediction options in PowerBI
Add the R code in the PowerBI model for the relevant data column
The R visualization can do predictive models of your choice
It is limited but very useful for business case scenarios
Recommended Blog post -
Revenue and forecasting by Christian Berg – Plot using R
https://community.powerbi.com/t5/Community-Blog/Revenue-and-forecasting/ba-p/86299
New Series of Time Series by PHD MVP Leiila Etatti –
http://radacad.com/new-series-of-time-series-part-1

35. Meanwhile in Azure ML Lab
Unfortunately, the ETS – Exponential smoothing module was deprecated, so lets
choose a better one!
Edit Metadata – Adding the column for the Average values
Split the data into sample and population (not just ignore last 10 but
randomize the split)
The question what is the average late time expected is simply wrong for
this tool, we would like to use it for actually predicting for each flight if it
is going to be late, or how the weather affects the flights being late.

36. Azure ML Lab some of the Mathematical models
Decision Forest Regression
Linear Regression (Excell as well…Solver)
2 Class Boosted Decision Tree
Decision Tree
2 Class Logistic Regression
Will be used in the prediction demo
to compare which is predicting the best way
K- Mean Clustering (PBI as well)

37. • Bullet one
• Bullet two
• Bullet three
The Prediction by Airport –
Hartsfield in Atlanta
Georgia and Chicago are
the 2 leading airports
that the weather has a
very large impact on the
delay times, the delay
times there are the
largest, just like we hear
in the news about those
airports being in delay
(How many Hallmark
movies are using the
weather in Chicago
airport during a
snowstorm in
Christmas…)

38. • Bullet one
• Bullet two
• Bullet three
The Flight Delay prediction compare the scored models
 So the blue prediction model
is slightly better than the red
one, to predict if the flight is
going to be late.
 Two class boosted decision
tree is slightly better than two
class logistics regression

39. Last Slide – What? When?

40. Azure ML
Data scientists, developers Business users, end users and customers,
Analysts friendly
Be the development platform for prediction
analytics solutions Development platform and publishing platform
for data visualization
Upload the data, manipulate the data, divide into
data set and training set, train the model,
evaluate the model create service, predict for
other data sets
PowerBI
Connect to data, create report, analyze exciting
data and get data insights
Who? What? Why?
Ask questions – Business users and managers
questions, evaluate, compare, classify, displayPredict given a mathematical trained model
based on past results
The next generation is already here… Azure IoT hub,
Azure AI and Machine learning focused on devs

41. Questions?

42. Thank you
for attending
@sqlpass
#sqlpass
@PASScommunity
Presenting Sponsor