Session on Microsoft office graph, graph theory, Azure machine learning, machine learning theory at the SharePoint saturday of Toronto 2015

1. Vincent BIRET
Tech Lead
@baywet bit.ly/vince365

2. GOLDRAFFLESILVE
R
PLATINU
M

3. 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
3

4. 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
4

5. 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
5

6. Graph Theory
MS Graph
Delve
MS Graph API
Machine learning theory
MS Azure ML
Conclusion
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7. 7

8. What is the Graph?
8

9. 9
0
1
2
3
4
5
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Category 1 Category 2 Category 3 Category 4
Chart Title
Series 1 Series 2 Series 3

10. 10
Sales
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

11. 11

12. RDBMS suck!.....
At doing what they are not meant
for.
12

13. 13
Vincent
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 as
Since 1/7/14
Works as
Since 12/7/12

14. Graphs can be represented by matrices
Very easy to compute by CPU’s
Low memory usage
14

15. 15

16. Data is in silos
Accessing different workloads is hard
Search doesn’t work
Points out new things
16

17. Unified API’s to:
Authentication
Files
Groups
Sites
Mails…
Search
The Office Graph
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18. 18

19. 19

20.  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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22. 24

23. 25

24. Machines can be trained to “guess stuff”
“They” can get better at doing it
Not AI but a step towards it
Not that new to the business world
26

25.  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
27

26.  Used to predict outcomes with few possible values
 Eg “married”, “divorced”….
 Eg “rev > 50K”, “rev < 50k”…
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27.  Used to predict continuous values
 Eg Potential profit of something
 Eg Potential time to achieve something
29

28.  You have data without expected results
 Build the experiment with a feedback loop
 Train it
 Put it in prod
30

29.  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
31

30.  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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31. 33

32. Now your applications can become
“clever” !!!
34

33.  Machine Learning* as a service
 * Mostly predictive and semantic analytics
 ML Studio
 Not an Expert System
35

34. Get data
Make an experiment
Test it
Generate a model
Publish an API
Integrate with your App
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35. 37

36. 38

37.  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
39

38. 40

39. Thanks very much for attending
my session!
Spbuzz.it/SPSTO2015-EVALS
Questions?
@baywet 41

40. Thank you!
Toronto Enterprise Collaboration User Group
Change Management, Governance, SharePoint, Office 365,
Yammer, PowerBI, etc
http://www.meetup.com/TSPBUG/
Toronto SharePoint Business Users Group
http://www.meetup.com/TorontoSPUG/
Saturday July 9, 2016
See you next year!