Main Takeaways - When to use ML in Products? - Is ML useful for your Product?

1. Building AI products
by Google Group Product Manager
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6. Building AI products
Dr. James Smith
@jws7
linkedin.com/in/jamessmith7

7. Story time

8. #1 most common question I heard
at DeepMind.
*Disclaimer - this is what I heard. It
may not have been what was asked
“I hear you
have Magic
Pixie dust. Can
I have some?”*

9. My experience
building AI
products

10. DeepMind for Google

13. Adaptive Brightness + Adaptive Battery

14. The hardest
problem in AI, isn’t
AI…

15. Android
Telemetry
Count things. Find bad numbers.

16. Building AI
products 101

17. Artificial Intelligence
Machine Learning
Deep Learning

18. PM’s role in ML product development
As a PM, generally you won’t be training models or choosing specific
technologies to use. As a PM what you will be doing:
Creating clarity on
the problem (User
Research, PRD, etc)
Setting success
criteria (PRD,
experiment design)
Advocating for
culture (privacy,
user respect, bias,
etc)
Communicating
with care (to users,
to stakeholders, to
execs)

19. When to use ML in
Products?

21. Is ML useful for your product?

22. Simplified Decision Tree: “Should I use ML?"
Do I have data that is suitable to power an ML solution?
Problem that cannot be solved with pre-programmed rules?
Does my product require personalization?
Will users tolerate errors in my product?
Are users comfortable not knowing exactly how a product functions?
Donʼt use ML Maybe try ML
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes

23. Building ML
products

28. Things to watch
out for

29. Preventing poor outcomes

30. TL;DR

31. It’s an incredibly powerful tool, that
needs to be used wisely.
PMs beware!
ML is not
Magic Pixie
Dust!

32. Learn more
Machine Learning Crash Course:
https://bit.ly/2lZ4Sdr
The Step-By-Step PM Guide to Building Machine Learning Based Products:
https://bit.ly/3RkXu5N
The Google Way to Use Machine Learning for PMs:
https://bit.ly/3KO3S36

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