This document discusses integrating predictive analytics, SharePoint, and Azure Machine Learning. It begins with an introduction to predictive analytics and machine learning. It then discusses Azure Machine Learning Studio for building machine learning models and creating web services. Finally, it discusses how machine learning models can be consumed in SharePoint Online. The key points are that Azure ML Studio allows easy creation of machine learning models, and these models can then be deployed as web services and consumed in applications like SharePoint.

1. Hottest Buzz Out There: Integrating Predictive
Analytics, SharePoint and Azure Machine Learning
Fernando Leitzelar, PMP
Vice President ITSM

2. THANK YOU
EVENT SPONSORS
We appreciated you supporting the
New York SharePoint Community!
• Diamond, Platinum, Gold, & Silver have
tables scattered throughout
• Please visit them and inquire about their
products & services
• To be eligible for prizes make sure to get
your bingo card stamped by ALL sponsors
• Raffle at the end of the day and you must
be present to win!

3. CONFERENCE MATERIALS
• Slides / Demo will be posted on Lanyrd.com
• http://lanyrd.com/2016/spsnyc
• Photos posted to our Facebook page
• https://www.facebook.com/sharepointsaturdaynyc
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4. • Visit ExtaCloud’s booth for wrist bands!
Scallywag's Irish Pub
508 9th Ave, between 38th & 39th.
[6 minutes walk]
Scallywags also serves food.
http://www.scallywagsnyc.com/

5. Speaker
Fernando Leitzelar, PMP
Vice President ITSM
Fernando Leitzelar is a senior SharePoint Evangelist and Vice-president with a Large Bank As a
consultant he regularly interfaced with clients and development teams to design SharePoint-based
solutions. Fernando has progressively held SharePoint positions ranging from developer and
administrator to Architect and Manager. He has been a SharePoint Saturday Speaker since 2010, having
worked extensively on designing and architecting sophisticated SharePoint based applications. He
maintains expertise in Office 365, Azure, SharePoint 2016/2013/2010/2007/2003, BI and Machine
Learning Solutions.
Twitter: @fleitzelar
Blog: http://sharepointusa.wordpress.com

6. • What and How ?
Introduction to ML and
Predictive Analytics
• Predictive Analytics using Machine LearningPredictive Analytics
• Machine Learning Studio
• Building ML models
• Create a Web Service
Azure Machine
Learning
• Consume Machine Learning ModelSharePoint Online
Agenda

7. Predictive Analytics
Predicting future performance based on historical data

8. Predictive analytics
encompasses a variety of
statistical techniques from
predictive modeling,
machine learning, and data
mining that analyze current
and historical facts to make
predictions about future or
otherwise unknown events.

9. ADVANCED ANALYTICS
BEYOND BUSINESS INTELLIGENCE

10. From Descriptive to Prescriptive
Analytics Maturity Level

11. What
happened ?
• Reporting:
Statistics
Why did it happen ?
• Analysis: Excel, OLAP
What is happening ?
• Monitoring: Dashboards,
Scorecards
What will happen ?
• Prediction: Data Mining,
Machine Learning
Evolution of Predictive Analytics
2000s
1990s
1980s
2010s

12. Machine Learning
Computer Systems that improve with experience

13. CLASSES OF LEARNING PROBLEMS
• Classification: Assign a category to each item (Chinese | French | Indian | Italian |
Japanese restaurant).
• Regression: Predict a real value for each item (stock/currency value, temperature).
• Ranking: Order items according to some criterion (web search results relevant to a
user query).
• Clustering: Partition items into homogeneous groups (clustering twitter posts by
topic).
• Dimensionality reduction:Transform an initial representation of items into a lower-
dimensional representation while preserving some properties (preprocessing of
digital images).

14. WHAT IS MACHINE LEARNING?
Methods and Systems that …
Adapt based
on recorded
data
Predict new
data based
on recorded
data
Optimize an
action given
a utility
function
Extract
hidden
structure
from the
data
Summarize
data into
concise
descriptions

15. MACHINE LEARNING IS NOT
Methods and Systems that …
can yield
Garbage-In-
Knowledge-
Out
perform good
predictions
without data
modeling &
feature
engineering
Silver-bullet
for all data-
driven tasks –
it’s a powerful
data tool!
are a
replacement
for business
rules – they
augment them!

16. TRANSFORMATIONAL

17. A Good Machine
Learning Tool
would allows us to
solve extremely hard problems
better
extract more value from Big Data
approach human intelligence
drive a shift in business analytics

18. Data Science is far too complex today
• Access to quality ML algorithms, cost is high.
• Must learn multiple tools to go end2end,
from data acquisition, cleaning and prep,
machine learning, and experimentation.
• Ability to put a model into production.
This must get simpler, it simply won’t scale!
PROBLEMS ML NEEDS TO ADDRESS …

20. PREDICTIVE ANALYTICS AND ML SCENARIOS
Predictive maintenance

21. Classification
Regression
Clustering
Anomaly Detection
AZURE ML ALGORITHMS

22. ADVANCED ANALYTICS TODAY
HARD-TO-REACH SOLUTIONS
Break away
from industry
limitations

23. WHAT HAS IT GOT TO DO WITH
SHAREPOINT ?
ML
Studio
API
M

24. AZURE MACHINE LEARNING

25. AZURE MACHINE LEARNING
Azure Portal
ML Studio
ML API Service
Operational
Team
Data Scientists and
Data Professionals
Software
Developers

26. AZURE ML STUDIO

27. MICROSOFT AZURE MACHINE LEARNING
Built for a cloud-first, mobile-first world

28. Step 1
• Data
Preparation
and Feature
Engineering
Step 2
• Train and
Evaluate
Model
Step 3
• Deploy Web
Service
BUILDING ML MODEL

30. RECEIVER OPERATING CHARACTERISTIC CURVE

31. ROC CURVE

32. ROC CURVE

33. Reduce complexity to broaden participation
MICROSOFT AZURE MACHINE LEARNING
FEATURES AND BENEFITS
• Accessible through a web browser,
no software to install;
• Collaborative work with anyone,
anywhere via Azure workspace
• Visual composition with end2end
support for data science workflow;
• Best in class ML algorithms;
• Extensible, support for R.

34. MICROSOFT AZURE MACHINE
LEARNING
FEATURES AND BENEFITS
Rapid experimentation to create a
better model
Immutable library of models, search discover and
reuse;
Rapidly try a range of features, ML algorithms
and modeling strategies;
Quickly deploy model as Azure web service to
our ML API service.

35. • https://azure.microsoft.com/en-us/documentation/articles/machine-learning-
algorithm-choice/
• https://azure.microsoft.com/en-us/documentation/services/machine-learning/
• Azure Machine Learning Essentials Book
• https://channel9.msdn.com/blogs/Cloud-and-Enterprise-Premium/Building-
Predictive-Maintenance-Solutions-with-Azure-Machine-Learning
• Channel 9
AZURE MACHINE LEARNING RESOURCES

36. KEY CONCEPTS
Data
Model
Parameters
Learning Prediction
Decision Making
Utility Function

37. STEPS TO BUILD A MACHINE LEARNING SOLUTION

38. AZURE DATA MARKET ML APPLICATIONS
• http://text-analytics-demo.azurewebsites.net/
• https://churn.cloudapp.net
• http://how-old.net/#