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Hardcore Data Science—in PracticeDr. Mikio L. Braun, Delivery Lead for Recommendation and Search
StrataConf 2016, London [email protected]
@mikiobraun tech.zalando.com
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
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London
Recommendations
Data Driven Recommendations• Collaborative
filtering • Content based
recommendation • Personalised
recommendations • …
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London
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
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
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
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
Data Scientists and Developers
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London
DS&D: CodingVery different approaches to coding…
← developers
data scientists →
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London
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
Organization• Cross-functional
teams • Communication! • Microservices, at
Zalando: STUPS (Docker on AWS)
Mikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London
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
servicesMikio Braun, Hardcore Data Science in Practice, Strata+Hadoop World 2016, London