Dima Boyko podczas AIMeetup #3 w Krakowie organizowanego przez 2040.io opowiadał o tym jak ważne są dane w machine learning.

1. Data is oxygen forML
Cracow, 8th December 2016

2. Hello, I’m Dima Boyko
Dima Boyko
Rails developer, Python developer, Data Scientist
Software engineer @inFakt
dima.boyko@infakt.pl
/dimaboyko

3. What computer can do better than human?
What humancan do better than computer?

4. What computer can do better than human?
+
What humancan do better than computer?

5. ”
Field of study that gives computers the ability tolearn
without being explicitly programmed
1959, Arthur Samuel
What computer can do better than
human?
+
What humancan do better than
computer?
Machine learning

6. Machine learning
is not the future

7. Machine learning
ALVIN
YouTube
https://youtu.be/ilP4aPDTBPE?t=39
1992 !

8. Machine learning

9. Boost
ML can boost existing products by
improving quality and usability ofsome
modules
Unlock
Using ML can unlock newproduct
use-cases
Machine learning

10. Machine learning
Drawbacks?

11. Machine learning
Drawbacks?
WOW / WTF ratio

12. Data Algorithm Insight
Machine learning
Usage model

13. Data
Machine learning
Usage model

14. Data

15. Using of data

16. Using of data
Red Roof Inn

17. 2 to 3%
of flights werecanceled
Using of data
Red Roof Inn

18. 500
daily
Using of data
Red Roof Inn

19. 90 000
passengers
Using of data
Red Roof Inn

20. Weather data
Using of data
Red Roof Inn

21. Using of data
Red Roof Inn
10%
more revenue during season

22. Using of data
Los Angeles Police Department

23. Historical
DATA
Analysis &
Prediction
Reaction
Using of data
Los Angeles Police Department

24. Using of data
Los Angeles Police Department

25. Using of data
Los Angeles Police Department

26. 33% 21%
Less thefts Less victims
Using of data
Los Angeles Police Department

27. Using of data
UPS Cargo Delivery

28. 16,9M
Delivered cargos daily
195
Countries around the globe
Using of data
UPS Cargo Delivery

29. Orion
• Mathematical model for operations research
• Huge processing power in realtime
Using of data
UPS Cargo Delivery

30. 13 000
Tons of exhausts
less
Using of data
UPS Cargo Delivery
6M
Litres less fuel
usage during
the year
+ Faster
deliveries

31. Using of data
inFakt
Automated
Accounting

32. ~50 000
Invoices booked monthly by accountants
Using of data
inFakt Automated Accounting

33. AutoAccounting
Brief product history
Using of data
inFakt Automated Accounting

34. •
•
•
Data from last year
Scikit-learn
Infrastructure
AutoAccounting
Classification
15% invoices
95% correct

35. •
•
Data from last year
Infrastructure
AutoAccounting
Classification
55% invoices
95% correct

36. AutoAccounting
Classification
Keep it simple

38. 3%
Wrong
Inconsistent
8/10
Human mistake
AutoAccounting

39. ~70%
Invoices booked automatically
AutoAccounting

40. ~70%
Invoices booked automatically
600 / month
Hours saved for creative work
AutoAccounting

41. Know your DATA!

42. Auto
What’s next?

43. Auto
What’s next?
#worldwide #vendor_independent #simple

44. What’s next?
Open Source ?
/OpenAutoX

45. Thanks! Any questions?
Dima Boyko
Software engineer @ inFakt
dima.boyko@infakt.pl
/dimaboyko