Major roles in an AI team include product managers, data scientists, machine learning engineers, data engineers, and IT/data infrastructure. Successful AI projects require cross-functional collaboration between these roles. Many AI projects fail due to data silos that prevent effective collaboration and evaluation of ROI. Change is constant, and effective AI deployment requires adapting to new challenges through experimentation and iteration.

1. / :
:
/
:

2. 2

3. 7/21
3

4. 4

5. 5

9. 8/18
9

10. 9/2
10

11. 9/29
11

12. 9/27
12

13. 13
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14. 14
( )

15. / 62:
15
5
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3 5
1
5
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400 x 80 3 5 x 4 6
= 80 4800 AI / / 9

16. ADVICES FOR
AI DEPLOYMENT
16

17. 17
/

18. 18

19. 19

20. Major Roles in an AI Team
22
Product Manager Data Scientist
Machine Learning
Engineer
Data Engineer

21. 23
IT / Data
Infrastructure
ML / DL
Skills
Data Engineer
Machine Learning
Engineer
Data Scientist
Domain
Expertise

22. 24
(coign of vantage)

24. /
X Y
X Y
Y X
X & Y
26
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vs.

25. 27

26. 28

27. 29
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28. e
c
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30https://rocket.cafe/talks/84164

29. : , ,
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32

30. /
10 (1/2)
33

31. 10 (2/2)
/
34

32. Why many AI projects failed?
(Data Silo)
( )
ROI

33. 41
/
A/BTest
+ A/BTest

34. 42

35. 43

36. 44

37. 45

38. 11/10/18
4
6
https://blog.optimizely.com/2015/06/04/ecommerce-conversion-optimization-case-studies/

40. Change is the only constant.
- Heraclitus (535 BC - 475 BC)
(Slide Credit: Albert Chen)

41. / 73(Credit: , AI )

42. / 75(Credit: , AI )

43. Camelyon Grand Challenge in 2016
The Winning Team
: 92.5
: 96.6
: 99.5
/
76

44. AI
AI / 1994 World Wide Web
ML / 1994 HTML and CGI
AI
AI AI
82

45. •
•
• 40
•
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47. AI + AI