Infrastructure powering online optimization workflow with model versioning for enhanced developer productivity.

1. Architecting a real-time
optimization platform for
Driver Positioning
Hao Yi Ong
w/ many others at Lyft
Applied AI Summit | San Francisco | June 20, 2019

2. Overview
TakeawaysFrameworkContext

3. Context

4. Two stories

5. Story 1: Conflict

6. Story 2: Truck, pen, and paper

7. Two ideas
Pre-empt general market imbalance
and help with demand spikes
Algorithmically coordinate drivers
when whole cities light up
De-conflicting drivers Early signaling

8. The official word

9. Demand you can
count on
Within your reach
Personal Power Zones (PPZs)
What you see is
what you get

10. Give a ride, get your bonus
#1

11. Drive into the purple zone,
guarantee a bonus on your next
ride.
#2

12. Drive into the pink zone, guarantee
an even bigger bonus
#3

13. Focus

14. Improve market efficiency
• Shaping: Reduce driver downtime with better
positioning
• Volume: Intelligently redistribute Growth budget
between our riders and drivers
‒ Scenario 1: Suburb oversupplied. Funnel budget
into rider incentives to increase demand and
keep utilization high.
‒ Scenario 2: City undersupplied. Rebalance
spend to get more drivers on the road.
Address driver PT product complaints
• Provide clearer and more reliable signal
• Better reward effort (vs. luck)
‒ Experienced drivers still get more money in the
same areas, but newer/part-time drivers will
benefit from the signal.
Why care?

15. Timeline for “Flex PPZ” framework
18Q3: Flex PPZ plan
Rollout still limited to 9
regions for EOY due to
issues. Drafted initial
framework for infra and
models in response to
problems at EOQ.
19Q1: 100% Flex PPZ
All regions successfully
transitioned to Flex PPZ. RS
oncall and OKR revamped.
Rollout accelerated.
18Q4: Flex PPZ live
Infra and model developed
from Flex PPZ framework.
Live in 5 regions by X’mas.
18Q1: v1 rollout
Model dev couldn’t
match ambitious rollout
and relied on manual
configs; couldn’t provide
a reliable experience.
19Q2: almost all
drivers on PPZ
Demonstrate reliable
product scalability with
sustained gains in 50+
regions.

16. Breakthrough requires
rapid iterations in
model development
Manual configs
impractical; need
algorithmic
breakthrough
Story
Change in
development
framework enabled
ambitious rollout

17. The grand scheme of things
● business rules
● ML model inference
● recommendation systems
On-demand execution
● ETL jobs
● ML model training
● graph analytics
Batched aggregate job

18. ● business rules
● ML model inference
● recommendation systems
● ETL jobs
● ML model training
● graph analytics
On-demand execution Batched aggregate job
● chemical process plants
● SpaceX reusable rockets
● Lyft driver positioning
Near real-time optimization
The grand scheme of things

19. Framework

20. Problem

21. Prior art
Manual configurations determine allocations that are valuable enough for PPZs to be sent.
disallow
allocations w/
value difference
below a manual
threshold
PPZ maps
allocate
value
forecasted demand
supply
supply
allocation
many signals

22. New approach
Incorporate a feedback loop using a principled optimization program.
feedback PPZ maps
allocate
value
forecasted demand
supply
refine supply
allocation
many signals

23. New approach + improved signals
New approach necessitated improved signals and the ability for multiple owners to quickly
test and iterate on them collaboratively.
feedback PPZ maps
allocate
value
forecasted demand
supply
refine supply
allocation
driver model
rider model
budget
...
forecasting

24. Development

25. 10x Sci/Eng
(handles all
implementation)
Prior development model
driver model
rider model
budget
...
forecasting
Carol
Bob
Dave
probably Jason
Alice
2k+ lines script
some service or
jupyter nb

26. lightweight
script for PPZ
New development model
driver model
rider model
budget
...
forecasting
Carol
(implements model)
Bob
(implements model)
Dave
(implements signal)
probably Jason
(magics stuff)
Alice
(implements model)
Hao Yi
(mostly claims credit)
“components” “orchestrators”

27. Some applications…
PPZ Demand Map Hot Spots

28. Demand Map
Platform-sharing multiplies productivity
driver model
rider model
budget
...
forecasting
Carol
(implements model)
Bob
(implements model)
Dave
(implements signal)
probably Jason
(magics stuff)
Alice
(implements model)
Someone
“components”
PPZ
Hot Spots
“orchestrators”
Hao Yi
Sometwo

29. Takeaways

30. Building the platform
is less about scaling a
solution than scaling
its development
The strategy is to
empower Scientists
to rapidly and
independently iterate
on models
Development model >> model development
Our challenges are as
much technological
as they are
sociological

31. Building the platform
is less about scaling a
solution than scaling
its development
The strategy is to
empower Scientists
to rapidly and
independently iterate
on models
Development model >> model development
It also has the nice side-effect of being “sexier”
than just coding things up for Scientists.
Our challenges are as
much technological
as they are
sociological

32. 32
Modularized
algorithmic
components
1
Forward-looking
code and model
boundaries
2
Minimal
configurations
3
Experimentation
-friendly
framework
4
Rough cut of design principles

33. ● Usually custom-built services
relying on licensed solvers
● At Lyft, we build the platform
and a suite of tools for
multiple products
Long history of development
frameworks and tons of ML platform
startups; e.g., TFX
Long history of tools; e.g.,
Hive/Spark and
Pregl/Giraph/GraphX
On-demand execution Batched aggregate job
Near real-time optimization
Revisiting Science DevOps

34. Work in-progress in collaboration with many teams at Lyft — join us!

35. Previous work
● From shallow to deep learning in fraud
● Fingerprinting fraudulent behavior
● Algorithmic fairness in fraud prevention
● Interview: AI at Lyft

36. Reach out!
haoyi@lyft.com