Engineering for a Science-Centric Experimentation Platform

TL;DR

Netflix developed a science-centric experimentation platform enabling data scientists to contribute code directly in familiar languages, supporting advanced analyses and causal inference, seamlessly integrating research and production workflows.

Contribution

The paper introduces a novel architecture for an experimentation platform that supports science-centric workflows and a unified causal inference approach using potential outcomes.

Findings

Platform architecture supports end-to-end scientific workflows.

Unified causal inference framework facilitates diverse statistical methodologies.

Enables direct code contributions in Python and R for data scientists.

Abstract

Netflix 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 data scientists from a wide range of backgrounds by allowing them to make direct code contributions in the languages used by scientists (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 utilize a case-study research method to 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.