These days, training of the Machine Learning models at the device Edge is still a risky endeavor. It is frequently considered a purely academic subject with little value for real-life product development. In her talk, Vera will challenge this misconception, talk about the advantages of learning at the Edge and guide you through the Edge learning decision-making framework and design principles. https://www.aicamp.ai/event/eventdetails/W2021102210

1. Productizing Learning
at the Edge
Vera Serdiukova
in/veraserdiukova/

2. Agenda
1 Should You Train at the Edge
2 Making Big Decisions
3 Getting Your Hands Dirty

3. Definitions
edge device – a device whose compute, memory, and
energy resources are constrained and cannot be
easily changed.
learning at the edge – (re)training of a machine learning
model on the edge device.
Source: https://arxiv.org/pdf/1911.00623.pdf

4. autonomy accuracy
speed
privacy
evolution
Edge AI
benefits
security

5. autonomy accuracy
evolution
Edge
Learning
benefits
privacy security

7. Source: https://www.socialthinking.com/-/media/Images/Products/should-i-or-shouldnt-i-middle-school-high-school-edition.ashx?mw=1000&mh=1000&hash=D717577174BB1A93808467296D0AD0106ED538C8

8. Should You Train at the Edge

9. Approaches
model
refinement

10. Approaches
collaborative
learning

11. Approaches
exclusively on the
device

12. Should I train at the Edge?

13. Do you have a clear understanding
of the target device constraints?

14. Do you have a clear understanding
of the target device constraints?
N

15. Don’t do it.
N

16. Don’t do it.
N
… but I can pick my own hardware

17. N
… but I can pick my own hardware
Are you/your users willing to pay
more for a potentially more
expensive device?

18. N
… but I can pick my own hardware
Are you/your users willing to pay
more for a potentially more
expensive device?
N

19. Don’t do it.
N

20. N
… but I can pick my own hardware
Are you/your users willing to pay
more for a potentially more
expensive device?
N
Y

21. Do you have a clear understanding
of the target device constraints?
Y
Y

22. Do you have a sufficient amount of
qualifying training data on the
device?
Y
Y

23. Do you have a sufficient amount of
qualifying training data on the
device?
N

24. Can you obtain the data you need
through the target device?
N

25. Can you obtain the data you need
through the target device?
N
N

26. Don’t do it.
N

27. Can you obtain the data you need
through the target device?
N
N
Y

28. Y
Y
Do you have a sufficient amount of
qualifying training data on the
device?
Y

29. Do the existing ML tools satisfy your
on-device training objectives?
Y
Y

30. Source: https://developer.apple.com/videos/play/wwdc2019/209/?time=647

31. Sources: https://coral.ai/docs/edgetpu/retrain-classification-ondevice-backprop/#overview
and https://coral.ai/docs/edgetpu/retrain-classification-ondevice/#overview

32. Source: https://blog.tensorflow.org/2019/12/example-on-device-model-personalization.html

33. Do the existing ML tools satisfy your
on-device training objectives?
N

34. Do you have an existing expertise in
Machine Learning and Embedded
Engineering and/or willing to invest
heavily?
N

35. Do you have an existing expertise in
Machine Learning and Embedded
Engineering and/or willing to invest
heavily?
N
N

36. Don’t do it.
N

37. Do you have an existing expertise in
Machine Learning and Embedded
Engineering and/or willing to invest
heavily?
N
N
Y

38. Y
Y
Do the existing ML tools satisfy your
on-device training objectives?
Y

39. Source: https://giphy.com/gifs/sesamestreet-sesame-street-50th-anniversary-kyLYXonQYYfwYDIeZl

40. Congratulations! Sounds like
Learning at the Edge might be an
eligible candidate for your needs.
Y
Y

41. Making Big Decisions

42. 1/
What do you want to learn
through training at the Edge?

43. personalization
or customization

44. universal
improvement

45. ultimate
independence

46. 2/
Where and how does the
learning happen?

47. personalization
or customization
model
refinement

48. Source: https://developer.apple.com/videos/play/wwdc2019/209/?time=660

49. universal
improvement
collaborative
learning

50. Source: https://blogs.nvidia.com/blog/2020/04/15/federated-learning-mammogram-assessment/

51. ultimate
independence
exclusively on the
device

52. 3/
When does the learning
happen?

53. frequency
trigger
device state

54. 4/
What is the role of your user in
the training process?

55. Source: https://giphy.com/gifs/animation-space-peekaboo-3o85xJWLC5vZc34Pss

56. Getting Your Hands Dirty

57. Design to obtain data

58. Source: https://machinelearning.apple.com/research/personalized-hey-siri

59. Enable data labeling
add a name

60. Accommodate training actions
customize

61. Build model validation mechanisms

62. Source: https://blog.google/products/search/gboard-now-on-android//
alternatives
“manual”
control
Design for course-correction

63. Should You Train at the Edge
Making Big Decisions
Getting Your Hands Dirty

64. thank you
Vera Serdiukova
in/veraserdiukova/