Paper Info
Title
Engineering for a science-centric experimentation platform
Abstract
ABSTRACTNetflix is an internet entertainment service that routinely employs experimentation to guide strategy around product innovations. As Netflix grew, it had the opportunity to explore increasingly specialized improvements to its service, which generated demand for deeper analyses supported by richer metrics and powered by more diverse statistical methodologies. To facilitate this, and more fully harness the skill sets of both engineering and data science, Netflix engineers created a science-centric experimentation platform that leverages the expertise of scientists from a wide range of backgrounds working on data science tasks by allowing them to make direct code contributions in the languages used by them (Python and R). Moreover, the same code that runs in production is able to be run locally, making it straightforward to explore and graduate both metrics and causal inference methodologies directly into production services. In this paper, we provide two main contributions. Firstly, we report on the architecture of this platform, with a special emphasis on its novel aspects: how it supports science-centric end-to-end workflows without compromising engineering requirements. Secondly, we describe its approach to causal inference, which leverages the potential outcomes conceptual framework to provide a unified abstraction layer for arbitrary statistical models and methodologies.
Year
DOI
Venue
2020
10.1145/3377813.3381349
International Conference on Software Engineering
Keywords
DocType
ISBN
experimentation,A/B testing,software architecture,causal inference,science-centric
Conference
978-1-7281-6524-0
Citations 
PageRank 
References 
0
0.34
14
Authors
7
Name
Order
Citations
PageRank
Diamantopoulos Nikos100.34
Wong Jeffrey200.34
Mattos David Issa300.34
Ilias Gerostathopoulos425426.55
Wardrop Matthew500.34
Mao Tobias600.34
McFarland Colin700.34