#SPSToronto Make graph data useful for your company
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Transcript of #SPSToronto Make graph data useful for your company
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MAKE GRAPH DATA USEFUL FOR YOUR COMPANY
Vincent BIRETTech Lead@baywet bit.ly/vince365
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GOLD
THANK YOU SPONSORS!RA
FFLE
SILV
ER
PLATINUM
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WHY SHOULD YOU CARE?Graph and Machine learning are going to be game changers for businesses in next 10 years
IOT is the next big wave
Not caring now would be like not caring about the cloud back in 2008
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WHO’S THAT SESSION FOR?Users who are tired of “stupid” and
isolated applications
Developers who want to ship awesome apps!
Deciders who want to make something out of their data
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TODAY’S OBJECTIVE (S)Understand what’s a/the graphUnderstand what are MS Graph and Delve
Understand why it’s a game changer for your business
Learn how to use it in your applicationsUnderstand what’s Azure Machine learning
Learn how to use it in your applications
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AGENDAGraph TheoryMS GraphDelveMS Graph APIMachine learning theoryMS Azure MLConclusion
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READY?
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GRAPH THEORY
What is the Graph?
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IS THAT A GRAPH?
Category 1 Category 2 Category 3 Category 40
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2
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Chart Title
Series 1 Series 2 Series 3
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IS THAT A GRAPH?Sales
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
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THAT’S A GRAPH!
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WHY GRAPHS?RDBMS suck!.....
At doing what they are not meant for.
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THE PROPERTY GRAPHVincent
Desk: E43
Phone: 514 444 4444
Extension: 275
Negotium
Street Address: Montreal
Creation : 1/1/00
Technical Advisor
Must do: technical advising
Advantages: better business cards
Developper
Must do: development
Advantages: better keyboard
Works asSince 1/7/14
Works asSince 12/7/12
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WHY ARE COMPUTERS SO GOOD WITH GRAPHS?Graphs can be represented by matrices
Very easy to compute by CPU’sLow memory usage
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THE MICROSOFT GRAPH
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WHY A MICROSOFT GRAPH?Data is in silosAccessing different workloads is hard
Search doesn’t workPoints out new things
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WHAT’S MICROSOFT’S GRAPH?Unified API’s to:
AuthenticationFilesGroupsSitesMails…
SearchThe Office Graph
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DEMO
DELVE
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MS GRAPH API
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STRUCTURE Data
Nodes Actors Edges
Some Edges Modified Viewed TrendingAround WorkingWith OrgManager OrgColleague
Edges properties ActorId ObjectId Action Type Time Weight
Node properties SharePoint Search
Schema Object model
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DEMO
MS GRAPH
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MACHINE LEARNING THEORY
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STATE OF THE ART
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HIGHLIGHTS
Machines can be trained to “guess stuff”
“They” can get better at doing itNot AI but a step towards itNot that new to the business world
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SUPERVISED LEARNING You have training data with expected results
You have control data with expected results
Build the experiment with a feedback loop
Train it
Put it in prod
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CLASSIFICATION Used to predict outcomes with few possible values
Eg “married”, “divorced”…. Eg “rev > 50K”, “rev < 50k”…
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REGRESSION Used to predict continuous values
Eg Potential profit of something Eg Potential time to achieve something
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UNSUPERVISED LEARNING You have data without expected results
Build the experiment with a feedback loop
Train it
Put it in prod
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CLUSTERING Used to detect natural grouping patterns of data
(ie: data that might be related together)
Produces groups of data and puts the data in it
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« MATCHMAKER » Ideal to match data together
Things like Movies you might like Items others bought Online dating (matching you with another person) …
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AZURE MACHINE LEARNING
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WHY SO IMPORTANT TO DEV’S?
Now your applications can become “clever” !!!
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HIGHLIGHTS Machine Learning* as a service
* Mostly predictive and semantic analytics
ML Studio
Not an Expert System
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METHODOLOGYGet dataMake an experimentTest itGenerate a modelPublish an APIIntegrate with your App
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DEMO
ML STUDIO
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CONCLUSION
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CONCLUSION Better integration between apps/workloads (Graph)
Better understanding of the data by apps (and predictive) (ML)
Better user experience/productivity
Happier users
Money saved for the company
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Q&AThanks very much for attending my session!
Spbuzz.it/SPSTO2015-EVALSQuestions?
@baywet
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Thank you!Toronto Enterprise Collaboration User GroupChange Management, Governance, SharePoint, Office 365, Yammer, PowerBI, etchttp://www.meetup.com/TSPBUG/Toronto SharePoint Business Users Grouphttp://www.meetup.com/TorontoSPUG/
Saturday July 9, 2016
See you next year!