This is an overview of machine learning from a product perspective. I define machine learning and AI, and use product examples to give an overview of how ML works in supervised learning, unsupervised learning, and deep learning. I reflect on how the product development cycle might differ when developing machine learning-enabled products, and conclude with a reflection on the hype of machine learning, and call-to-action to consider ML for your products. I gave this talk at the sold-out Women in Product event called Building Machine Learning Products on July 27, 2017 at Squarespace HQ.

1. MACHINE LEARNING FOR
PRODUCT MANAGERS
I N G A C H E N

2. A G E N D A
1. C o n c e p t s o f M L f o r P M s
2. S q u a r e s p a c e P r o d u c t D e v C y c l e f o r M L P r o d u c t s
3. H y p e

3. Millions of miles
of driving
Millions of websites
Slice of the internet
Drivers
Fuel efficiency
Car Maintenance
SMBs
Online presence
and business

4. Millions of miles
of driving
Millions of websites
Slice of the internet
Create an optimized
experience for each user
MACHINE LEARNING

5. W H AT I S
M A C H I N E
L E A R N I N G ?

6. Artificial Intelligence
Human Intelligence Exhibited by Machines

7. GOALS OF AI:
Reasoning
Knowledge
Planning
Learning
Natural
Language
Processing
Perception
Move &
manipulate
objects
Artificial Intelligence
Human Intelligence Exhibited by Machines

8. “[Machine Learning is the]
field of study that gives computers the
ability to learn without being explicitly programmed
Arthur Samuel, 1959

9. “[Machine Learning is the]
field of study that gives computers the
ability to learn without being explicitly programmed
(often more data than is humanly possible to go through)
and makes a determination or prediction
about something in the world.
by observing data

10. H O W D O E S
M A C H I N E
L E A R N I N G
W O R K ?

11. INPUT OUTPUT
ML
MODEL
EMAIL
SPAM
NOT SPAM

12. DATA
EMAILS

13. Labeled data
IDEALLY…
EMAILS LABELED AS SPAM / NOT SPAM

14. 70%
Training Set
Validation
Set
30%
TEACHER GIVES EXAMPLES
TO ILLUSTRATE A CONCEPT
TEST

15. INPUT OUTPUT
F1 SCORE
CONFIDENCE LEVEL
ML
MODEL

16. S U P E R V I S E D
L E A R N I N G

17. P R O B L E M
Identify objects in billions of photos
Google Image Search

18. DATA
UNSEEN
DATA
LABELED DATA
INPUT OUTPUT
PREDICTED LABELS
CONFIDENCE %
CAT CAT
NOTNOT
NOT
CAT
NOT CAT
SUPERVISED
LEARNING
ML

19. U N S U P E R V I S E D
L E A R N I N G

20. P R O B L E M
Telling users where to improve their fuel
efficiency based on their driving behavior

22. 8 clusters

23. What distinguishes those 8 clusters?

24. What are the biggest factors affecting
fuel efficiency of our drivers?
Aggression on highway
Impatience on local roads
Weather, terrain, traffic

25. NY Cabbie Grocery Go-Getter
Highway Hustler Racecar Driver Suburban Scout
Foxy Fighter Pilot
Peaceful Sage
Commuter

27. P E R S O N A L I Z E I N S I G H T S
F O R E A C H U S E R

28. D E E P
L E A R N I N G
N E U R A L N E T W O R K S

29. S o u r c e : N V I D I A b l o g , M i c h a e l C o p e l a n d

30. P R O B L E M
How do we become familiar with such a
wide range of customers to build them the
products they need?
Visual Search Engine for Websites

32. Deep Learning: Under the hood (SIMPLIFIED)
Convolutional variational auto-encoder
Recreate the image
Find
similarities
Learns how to interpret the
structure of an image
hidden layers
DEEP

35. B U I L D I N G
M L P R O D U C T S

36. INPUT OUTPUT
MANY ML MODELS
PRODUCTS IN PRACTICE

37. Voice -> Text
Text -> Tasks
Alexa, turn on the lights.

38. Interpreting audio
User listening history
“I wish my significant other understood
me as well as Discover Weekly.”

39. O P T I M I Z E D
E X P E R I E N C E F O R E A C H U S E R

40. PRIORITIZE BUILD LAUNCHLEARN MEASURE
FOR SOFTWARE PRODUCTS USING ML
P R O D U C T D E V E L O P M E N T C YC L E

41. PRIORITIZE BUILD LAUNCHLEARN MEASURE
FOR SOFTWARE PRODUCTS USING ML
P R O D U C T D E V E L O P M E N T C YC L E
CLASSIFY AS ML
CONSIDER NON-ML
GATHER &
PREP DATA
PRODUCT CHANGES?
BUILD & EVALUATE
MODELS
THROW AWAY
IDENTIFY MODEL
WEAKNESSES
COLLECT MORE DATA
REBUILD & IMPROVE
LAUNCH
PRODUCTION MODEL
ACCURACY %

42. O R G S T R U C T U R E
2 Directors
11 Product Managers
1 UX Researcher
1 Team Lead
3 ML Engineers
1 ML Intern
ML TEAM PRODUCT TEAM
57% WOMEN40% WOMEN

43. MACHINE
LEARNING
PRODUCTS
MATURING OF ML
ALGORITHMS
with open source tools and
deep learning
MASSIVE
COMPUTE POWER
Cloud computation
GPU processing
ACCESSIBILITY OF
OPEN DATA
CommonCrawl - petabytes
ImageNet - 14M labeled
W H Y M L N O W
YOUR COMPETITORS ARE
DOING IT
NOT JUST FOR
BIG COMPANIES
EVEN
INDIVIDUALS
CAN
DO
IT

44. I N G A C H E N
@ingachen
inga.h.chen@gmail.com