Data has always been crucial to the growth of Bolt. One of the fastest-growing European companies, it challenges US giants whose engineering teams are orders of magnitude larger. In this talk, I’ll share how development of Data Science Platform enables us to grow fast and make Machine Learning in production accessible and reliable. website: https://fwdays.com/en/event/data-science-fwdays-2019/review/scaling-data-science-at-bolt

1. Scaling Data Science @ Bolt
Denys Kovalenko

2. Let’s talk about ML in
production

3. Walk Pool Scooter

6. Bolt growth
in less than 3 years
3M ➟ 25M Riders
100 ➟ 1000+ Team
11 ➟ 30+ Countries
2014 2016 2018

8. Data Science applied in:
● Marketing
● Pricing
● Dispatching
● Fraud prevention
● Maps
● CS automation
● ….

9. Scaling Data Science

10. Based on Googlers’ informal survey (Jan 2019)
Expectation
Reality
Defining KPI’s
Collecting Data
Developing Infra
Optimizing models
Integration
0.75 1
Time spent in Machine Learning project

11. Based on Googlers’ informal survey (Jan 2019)
Expectation
Reality
Defining KPI’s
Collecting Data
Developing Infra
Optimizing models
Integration
0.75 1
Time spent in Machine Learning project

12. Time spent in Machine Learning project
Expectation
Reality Optimizing models
0.75 1
Defining KPI’s Optimizing models

14. The rest of the owl
ML: drawing an owl
Hidden Technical Debt in Machine Learning Systems, NIPS 2015

15. How do we scale Data
Science?

16. Data Science Platform in Development
Data Lake
Data Quality
Preprocessing Model Lifecycle
Data Exploration
Services Services

17. Data Science Platform in Development
Data Lake
Operational
Warehouse
Data Quality
Services

18. Data Science Platform in Development
Data Lake
Operational
Warehouse
Data Quality
Preprocessing
Pipelines
ETLs
Feature Store
Services

19. Data Science Platform in Development
Data Lake
Operational
Warehouse
Data Quality
Preprocessing
Pipelines
ETLs
Feature Store
Model Lifecycle
Train
Deploy
Scale
Data Exploration
Services Services

21. Time spent in Machine Learning project
Expectation
Reality
0.75 1
* Getting features to model

22. Feature store
● Single place to define and explore features used
for ML.
● Easy to reuse existing features for new models.
● No changes in code of service consuming
predictions.

23. How it started - V1.
Redshift
(Data Warehouse) Cron every 24h;
Hard-coded queries

24. { name: "ride_count",
owner: "denys@bolt.eu",
group: "rider",
query: ” SELECT user_id AS id,
COUNT(*) AS ride_count.
FROM orders
GROUP BY user_id ”,
features: [ {name: "id"},
{name: "ride_count",
description: "Total previous ride count",
data_quality_checks: [{..}]}
]}

25. V2.0
Redshift S3
1. Compute & Store
2. Data Quality checks

26. V2.0
Redshift S3
1. Compute & Store
2. Data Quality checks
Message Broker

27. V2.0
Redshift S3
API
1. Compute & Store
2. Data Quality checks
Message Broker

28. V2.0
Redshift S3
API
1. Compute & Store
2. Data Quality checks
Message Broker

29. Results:
● Faster idea-to-production cycle.
● Reduced job time by 80%.
● Feature reusability.
● Used not only for ML.
● Models trained purely from existing features!

30. Results:
● Faster idea-to-production cycle.
● Reduced job time by 80%.
● Feature reusability.
● Used not only for ML.
● Models trained purely from existing features!

31. Results:
● Faster idea-to-production cycle.
● Reduced job time by 80%.
● Feature reusability.
● Used not only for ML.
● Models trained purely from existing features!

32. Results:
● Faster idea-to-production cycle.
● Reduced job time by 80%.
● Feature reusability.
● Used not only for ML.
● Models trained purely from existing features!

33. Results:
● Faster idea-to-production cycle.
● Reduced job time by 80%.
● Feature reusability.
● Used not only for ML.
● Models trained purely from existing features!

34. Time spent in ML project
Reality
New Reality
0.75 1

35. Key takeaways:
● Find most impactful problems

36. Key takeaways:
● Find most impactful problems
● Decide what to build vs what to buy

37. Key takeaways:
● Find most impactful problems
● Decide what to build vs what to buy
● Focus on 20/80 and iterate!

38. How you can get better
at Data Science?

39. We are hiring!
careers.bolt.eu
medium.com/bolt-labs
Denys Kovalenko

40. Q & A
careers.bolt.eu