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.
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Integrating Predictive Analytics, ML and SharePoint with Azure
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
• Tweet Us - @SPSNYC or #SPSNYC
• Sign Up for our NO SPAM mailing list for all conference
news & announcements
• http://goo.gl/7WzmPW
• Problems / Questions / Complaints / Suggestions
• Info@SPSNYMetro.com
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
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.
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
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!
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 …
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.