This document discusses how to scale data science products rather than data science teams. It presents examples of common problems faced when scaling products and classifies them as either product design problems, software engineering problems, or mathy/machine learning problems. The key issues discussed include managing user expectations, maintaining many models, using shared code across customer bases, testing accuracy in new markets, addressing cold starts for unknown customers, and identifying feedback loops.

1. SCALING DATA SCIENCE PRODUCTS
NOT DATA SCIENCE TEAMS
long winded views of scaling up
@amcasari
MLconf seattle, 2016may20
nasa
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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. @
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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?
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6. product design problem:
managing user expectation
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7. how are we going to maintain
so many more models?
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8. software engineering problem:
scale a code base
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9. how can we use the same code when
we have different customer bases?
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10. mathy ML problem:
feature design
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11. how accurate will your product
be in the new market?
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12. software engineering problem:
test test test
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13. how are you going to be personalized
for a customer base you don’t know?
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14. mathy ML problem:
cold start
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15. how do we know you are right?
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16. product design problem:
identifying feedback loops
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17. {THANKS MUCH}
amanda.casari@concur.com
@amcasari