The document discusses the detection of a male individual with specific attributes and facial landmarks, as well as the verification result showing low confidence in identity matching. It also outlines the services of a food company catering to various sectors, emphasizing customer churn prediction and the efficiency improvements in handling mail by the United States Postal Service through advanced analytics and machine learning. The aim is to make machine learning accessible across different enterprises and users globally.

2. https://aka.ms/cs492

4. Who is he?

10. Detection Result:
JSON:
[
{
"faceRectangle": {
"width": 109,
"height": 109,
"left": 62,
"top": 62
},
"attributes": {
"age": 31,
"gender": "male",
"headPose": {
"roll": "2.9",
"yaw": "-1.3",
"pitch": "0.0"
}
"faceLandmarks": {
"pupilLeft": {
"x": "93.6",
"y": "88.2"
},
"pupilRight": {
"x": "138.4",
"y": "91.7"
},
...

11. Verification Result:
JSON:
[
{
"isIdentical":false,
"confidence":0.01
}
]

17. • Customers
• Fast food +
• Retail
• School food
• Hotel
• Catering
• Year 2014
• 8 subsidiary
• 60,000 customers
• 4,500 products
• 1M + purchase orders
JJ Food Service

18. • top 20% churn prediction
3x more revenue(Prev.
random)
• 200+ churn prediction,
45% prevent user = 17%
churn
JJ Food Service

19. The United States Postal Service
processed over 150 billion pieces of
mail in 2013—far too much for
efficient human sorting.
But as recently as 1997, only 10% of
hand-addressed mail was
successfully sorted automatically.

20. challenge

21. Learning various hand-writing and
recognize
- 98%+ accuracy

23. Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Prescriptive
Analytics
What
happened?
Why did
it happen?
What will
happen?
How can we
make it happen?
Traditional BI Advanced Analytics

30. Machine learning
DEMO – building training model

32. Make machine learning accessible to every
enterprise, data scientist, developer,
information worker, consumer, and device
anywhere in the world.
Azure Machine Learning target