MLConf Seattle 2016 Talk - Thoughts on how to scale data science products as design, engineering and people problems

1. SCALING DATA SCIENCE PRODUCTS
NOT DATA SCIENCE TEAMS
long winded views of scaling up
@amcasari
MLconf seattle, 2016may20
nasa
@

2. data science via random walks
senior product manager +
senior data scientist
@ Concur Labs
control systems
engineering +
robotics + legos
officer in USN
operations research
analyst
wandering dirtbag +
conservation volunteer
EE +
applied math
+ complex systems
underwater robotics
consultant
extraordinaire
SAHM

3. @
?

4. • Is this a Product Design problem?
• Is this a Mathy ML problem?
• Is this a Software Engineering problem?
@
data science therapy:
let’s talk about your problems

5. how do we keep
new customers happy?
@

6. product design problem:
managing user expectation
@

7. how are we going to maintain
so many more models?
@

8. software engineering problem:
scale a code base
@

9. how can we use the same code when
we have different customer bases?
@

10. mathy ML problem:
feature design
@

11. how accurate will your product
be in the new market?
@

12. software engineering problem:
test test test
@

13. how are you going to be personalized
for a customer base you don’t know?
@

14. mathy ML problem:
cold start
@

15. how do we know you are right?
@

16. product design problem:
identifying feedback loops
@

17. {THANKS MUCH}
amanda.casari@concur.com
@amcasari