1. Nick Hauenstein Signals, Intelligence, and Intelligent Actions
I am a Microsoft employee, but I had nothing to do with building any of the Microsoft products featured. All of my customers are internal. I wrote this talk before I was employed by Microsoft. I wrote it as a technology enthusiast. I am an expert in Logic Apps, but not in Azure ML or the human brain.
If you have any specific questions about this talk, or for more info on the device wiring / code / etc…, you can reach me via at
Enjoy!

2. Self-Driving Cars

3. HoloLens

4. Chihuahua or Muffin?

5. News Article

6. Their Process

7. Data Scientists vs. Data Pragmatists

8. My Funding

9. My Process

10. demo Machine Learning Mind Reading

11. Practical Machine Learning for Every Day Developers

12. Types of Problems Machine Learning Can Address
Top 5 Questions Machine Learning Answers
Is it A or B?
Is this weird?
How much/many?
How is this organized?
What should I do next?
Is it A or B?
e.g., Loan that will default, or that will pay out
Is this weird?
e.g., Was the transaction fraudulent
How much/many?
e.g., Best offer example
How is this organized?
e.g., Trending topics on social media
What should I do next? (Nothing available in Azure ML)
e.g., Self-driving cars, self-playing video games, etc…

13. How Does Machine Learning Work?
Machine Learning is Learning by Example
The goal is to construct a predictive model
Black box function that takes my inputs, and predicts the correct output (based on previous experience)
Many algorithms exist for coming up with a model

14. example Building a Predictive Model

15. Scenario: Buying Coins at the Best Price

16. (What we’re predicting)
Predicting Best Offer
Feature
(Column)
List Price
Accepted Offer
$1,250.00
$1,240.00
$1,200.00
$1,260.00
$1,225.00
$1,300.00
$1,255.00
$1,245.00
$1,285.00
$1,297.00
$1,238.00
$1,180.00
$1,160.00
Label
(What we’re predicting)
Sample
(Row)

17. Predicting Best Offer: Plot the Values

18. Predicting Best Offer: Plot the Trendline

19. The Linear Predictor Function

20. Our Completed Predictive Model
Note: This model is trying to predict human behavior which is inherently often erratic and unpredictable.

21. What If?
List Price
Accepted Offer
$1,250.00 ?

22. Using Our Predictive Model
$1,236.30
$1,221.04
$1,205.78
Note: This model is trying to predict human behavior which is inherently often erratic and unpredictable.
$1,250.00

23. Achievement Unlocked?

24. More Data + More Algorithms = Need More Power
List Price
Accepted Offer
$1,250.00 ?

25. procedure Hello AzureML

26. Typical First “Experiment”
Score Model
Data Source
Split Data
Algorithm
Evaluate
Module
(Step in the flow)
Train Model

27. Typical Second Experiment

28. Comparing Models in Azure ML Studio
This is the experiment used while trying to find the best algorithm for EEG signal classification. None of them ended up with a high degree of accuracy, but Two-Class Logistic Regression did end up better than a coin-flip.

29. demo Hello AzureML
DayOfWeek, Carrier, OriginAirportID, DestAirportID, DepDel15, ArrDel15

30. Where Do We Go From Here? Putting the Pieces Together
Azure Data Factory helps you construct clean data sets
Azure ML helps you train, evaluate, and operationalize models
Logic Apps bring the data in-flight to the predictive service
Signals – Your data itself, doesn’t do you very much good to just store it.
Intelligence – Being able to make predictions upon your data, that’s starting to move in the right direction, but unless you do something with those predictions, you may still not have derived the greatest value.
Intelligent Actions – As Integration developers, we are positioned exactly in the place to make all of the previous valuable. Signals are the most valuable to us when we apply intelligence to understand what they can tell us and/or predict, so that we can then take intelligent actions. Logic Apps + BizTalk are the places where we have both the raw data flowing through with which to make predictions, and where we have all of the tools at our disposal to take intelligent actions based on the predictions.
Having ML alone is like having a brain without a body. Data Scientists aren’t the *only* important people in an intelligent solution. Intelligent solutions are a team sport that requires everyone.

31. Who just said all of those words at me?
Nick
Senior Program Manager, Microsoft
I’ve only been here 3 weeks
Creator/maintainer of the T-Rex Metadata Library for Logic Apps / Power Apps / Flow
Former
I am a Microsoft employee, but I had nothing to do with building any of the Microsoft products featured. All of my customers are internal. I wrote this talk before I was employed by Microsoft as an enthusiast. I am an expert in Logic Apps, not in Azure ML or the human brain.
Former