Sps ottawa 2016 vincent biret - Microsoft graph and machine learning

WELCOMESHAREPOINT SATURDAY

OTTAWA

December 3rd, 2016

Vincent Biret

Make Graph Data useful for your company

House keeping

SPS Ottawa is made possible by our Sponsors!Platinum

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6 |SharePoint Saturday Atlanta

ShareP ntSummerhays Grill

5:30 pm1971 Baseline Road (corner of Woodroffe)

Please drink responsibly . We will be happy to call a cab for you

Vincent BIRETOffice Servers And Services [email protected]/vince365

Products Team Tech Lead

Montreal

About Me


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

Why should you care?


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

Who’s that session for?


Understand what’s a/the graph Understand what are MS Graph and Delve Understand why it’s a game changer for your

business Learn how to use it in your applications Understand what’s Azure Machine learning Learn how to use it in your applications

Today’s objectives


Graph Theory MS Graph Delve MS Graph API Machine learning theory MS Azure ML Conclusion

Agenda


Ready?

What is The Graph?

Graph Theory


Is That a graph?

Category 1 Category 2 Category 3 Category 40

1

2

3

4

5

6

Title

Series 1 Series 2 Series 3

Sales

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr


That’s a Graph!


RDBMS’s Suck!....

At doing what they are not meant for.

Why Graphs?


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


Graphs can be represented by matricesVery easy to compute by CPU’sLow memory usage

Why are computers so good with Graphs?

The Microsoft Graph


Data is in silosAccessing different workloads is hard

Search doesn’t workPoints out new things

Why a Microsoft Graph?


What’s Microsoft’s Graph?


WebHooksOpenType extensions

SharePoint (Sites/Lists/ListItems)

Org contactsDirectory

(everything in AAD)

Identity Protection

Tasks (planner)OneNote

Latest News?


Graph.microsoft.ioResources

Delve

Demo

MS Graph API


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

Structure

MS Graph API

Demo

Machine Learning theory


State of the art


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

Highlights


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

Supervised learning


Used to predict outcomes with few possible values

Eg “married”, “divorced”….Eg “rev > 50K”, “rev < 50k”…

Classification


Used to predict continuous values

Eg Potential profit of somethingEg Potential time to achieve something

Regression


You have data without expected results

Build the experiment with a feedback loop

Train it

Put it in prod

Unsupervised learning


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

Clustering


Ideal to match data together

Things likeMovies you might like Items others boughtOnline dating (matching you with another person)

…

« Matchmaker »

With great power comes great responsabilities

Azure Machine Learning


Now your applications can become “clever” !!!

Why so important to dev’s?


Machine Learning* as a service

* Mostly predictive and semantic analytics

ML Studio

Not an Expert System

Highlights


Get dataMake an experimentTest itGenerate a modelPublish an API Integrate with your App

Methodology

ML Studio

Demo

Time to day goodbye

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

Conclusion


Vincent Biret @baywetBit.ly/[email protected]

Questions & Answers / Thanks

Technology

Sps ottawa 2016 vincent biret - Microsoft graph and machine learning