The document introduces Azure Machine Learning, focusing on its capabilities for predictive analytics and integration with business intelligence. It outlines machine learning concepts, real-world case studies, and showcases Azure ML's features like a user-friendly interface and the incorporation of R and Python. Key topics include the machine learning process, data preparation, and deployment, also emphasizing the importance of good data and data culture.

1. Azure Machine Learning:
Welcome to the future of predictive
analytics
Ruben Pertusa Lopez
Microsoft SQL Server MVP
Data Platform Architect at SolidQ
rpertusa@solidq.com
Twitter: @rpertusa

2. Rubén Pertusa
 MS SQL Server MVP
 Data Platform Architect SolidQ
 Phd Candidate on Data mining
 SQLSaturday Barcelona founder
rpertusa@solidq.com
Twitter: @rpertusa

3. Say Thank you to Volunteers:
 They spend their FREE time to give you this
event.
 Because they are crazy. 
 Because they want YOU
to learn from the BEST IN THE WORLD.
 If you see a guy with “STAFF” on their back –
buy them a beer/wine, they deserve it.

4. Ivan Daniel Campos:

5. Rui Barreira:

6. Paulo Matos:

7. Pedro Simões:

8. André Batista:

9. 3 Sponsor Sessions at 15:05
 Don’t miss them, they might be getting
distributing some awesome prizes!
 Rumos
 BI4ALL
 Devscope

10. Our Main Sponsors:

11. Goals
This session is about:
 Introduction to ML and AzureML
 Real ML Cases
 Integration between AzureML and BI
This session is NOT about:
 Deep Dive in Data Science and R
 Building the best ML model
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12. Agenda
 ML Overview
 Real ML Cases
 AzureML Overview
 Demos! Demos! & Special Demo!
 BI feeds AzureML
 AzureML feeds BI
 Conclusions
 Questions
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13. MACHINE LEARNING
OVERVIEW

14. What is Machine Learning?
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System that can learn from data
and discover patterns and rules in
order to exploit important business
relationships

15. History of ML (and the BI Story)
Deep neural
Networks
No
improvements
Big Data
explosion
Graphical
models
SSAS DM
improvements
Scoring
Systems
SSAS 2000
DM features
Expert Systems
& Decision Trees
Neural
Networks

16. 2014 = Perfect Timing
Cheap & Scalable computing (Big Data)
+
Best ML algorithms
+
Data culture adoption
=
Move ML to the next level

17. Basic Problem: Text recognition

18. Transform it into a ML solution
Cleaned & Labeled data ML model trained Score input

19. One ML model to rule them all…?

20. Some experiences with ML
 BBC Case Study
 SSAS Performance Issue Detection
 Big Automotive Manufacturer: Customer
loyalty campaign & Stock calculator.
 Retail Company: Automate decision making

21. BBC: Case Study
 Input
 EntryId
 Date
 UserId
 SiteId
 ForumId
 ThreadId
 ParentId
 PrevId
 NextId
 Text
 Case table
 1.- Thread ( % Fails in a certain thread)
 2.- User (% Fails per User)
 3.- Diff Hour Forum Created (TimeDatePosted-
TimeForumCreated)
 4.- User Forum (% Fails in a certain forum)
 5.- Diff Last for User (TimeDatePosted - TimeLastFailUser)
 6.- Hour of the day
 7.- Diff hour UserJoined-Now (TimeDatePosted-TimeUserJoined
 8.- User Thread (% Fails per User in a thread)
 9.- Diff Hour Thread Created (TimeDatePosted-
TimeThreadCreated)
 10.- Day of Week
More than 200 attributes.

22. SSAS Performance issue Detection
 Goal: Predict when is going to fail
 Steps
 Monitor and collect all counters, events
 Label errors
 ML Classification & Time series algorithm

23. Customer loyalty campaign & Stock
calculator
 Big Automotive Manufacturer:.

24. Retail Company:
Automate decision making

25. More ML solutions
 Churn analysis
 Advertising analysis
 Pricing analysis
 Weather forecasting
 IT optimization
 Fraud detection
 Recommendation
engines
 Personalized services
 Health issues
detection
No limits

26. And Now…
AZURE ML

27. AzureML
 Fully-managed & scalable cloud service
 Focus on ability to develop & deploy
 For emerging data scientists
 UI for Data Science workflow
 Quality ML algorithms
 Collaborative
 Accessible through a web browser
 Fastest deploy to production

28. First look at AzureML
DEMO

29. CRISP Model
CRISP = Cross Industry Standard Process for Data Mining
(http://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining)
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30. AzureML Process Cycle
Get/Prepare
Data
Build
Experiment
Run
Experiment
Review
results
Save
Trained
Model
Add Trained
Model to
new
Experiment
Run Scoring
and set Public
Input/Output
Publish
Web
Service
Deploy
to Prod.
Data Scientist IT

31. Data Scientists love R
 Most powerful statistical programming
language
 Almost 400 of the most popular R Packages
already available and integrated
 Visualization using R plotting libraries
 Future:
 Upload your own R packages
 Python compatibility
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32. R integration
DEMO

33. AzureML Pricing
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34. Special demo
DEMO PORTOFLIX

35. BI feeds AzureML
 Case table is critical
Historical
Dataset
Cube
ETL
Mining Models
Cube

36. AzureML feeds BI
 Consume results from AzureML
 Azure Market Place
 C#, R,
 Excel addin
 Power Query
 http://microsoftazuremachinelearning.azurew
ebsites.net/

37. Power Query consuming AzureML
DEMO

38. Summary
 Convert problems into ML problems
 All about good data
 AzureML + Big Data + Data culture
Resources
 Machine Learning Blog
http://blogs.technet.com/b/machinelearning/
 Forum
http://social.msdn.microsoft.com/forums/azure/e
n-US/home?forum=MachineLearning
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39. QUESTIONS

40. Contact me!
 Rubén Pertusa López (rpertusa@solidq.com)
Twitter: @rpertusa
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Thank you!
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