Dr. Mikio L. Braun gave a presentation on hardcore data science in practice at StrataConf 2016 in London. He discussed how Zalando, an online fashion retailer operating in 15 countries, heavily uses data science for recommendation engines. Braun covered different recommendation techniques including collaborative filtering, content-based recommendations, and personalized recommendations. He also discussed challenges in moving from static data analysis to production systems that operate in real-time and are frequently updated and monitored. Additionally, Braun addressed collaborations between data scientists and developers who have different coding approaches, and advocated for cross-functional teams and microservices in organizations.

1. Hardcore Data Science—
in Practice
Dr. Mikio L. Braun, Delivery Lead for Recommendation and Search
StrataConf 2016, London
mikio.braun@zalando.de
@mikiobraun
tech.zalando.com

2. Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London
• 15 countries, 3 warehouses, 16+
million customers, 3bn€ revenue in
2015, …
• Heavily using data science for
recommendation

3. Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London
Recommendations

4. Data Driven Recommendations
• Collaborative
filtering
• Content based
recommendation
• Personalised
recommendations
• …
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London

5. For Example, One-pass Ranking Models
(Freno, Jenatton, Saveski, Archambeau, “One-Pass Ranking Models for Low-Latency Product
Recommendations”, KDD 2015)
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London

6. Hardcore Data Science to Production
• Usually one shot
computation
• Sometimes done
in Python
• Getting raw data
hard initially
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London

7. Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London
Production System
• Realtime system
• Usually done in Java/
JVM based
• Events and article data
continually upgraded

8. Data Science vs. Production
• A/B Test
offline
evaluation
• Iterate on data
science part
• Iterate on the
whole system!
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London

9. Data Scientists and Developers
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London

10. DS&D: Coding
Very different
approaches to
coding…
← developers
data scientists →
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London

11. DS&D: Collaboration
• What is the
most productive
way?
• Ideally, interface
on code, not just
documentation
• Production logs
often become
data analysis
input!
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London

12. Organization
• Cross-functional
teams
• Communication!
• Microservices, at
Zalando:
STUPS (Docker on
AWS)
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London

13. Summary
• “Static” Data Analysis vs. Production: Real-time,
frequently update & monitor.
• Facilitate fast iteration of data analysis &
production system.
• Data Scientists and Developers: Different
approaches, find a common ground
• Organizations: Cross-functional teams, micro
services
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London