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arXiv · 2101.04025

Distributed Double Machine Learning with a Serverless Architecture

Malte S. Kurz
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Abstract

This paper explores serverless cloud computing for double machine learning. Being based on repeated cross-fitting, double machine learning is particularly well suited to exploit the high level of parallelism achievable with serverless computing. It allows to get fast on-demand estimations without additional cloud maintenance effort. We provide a prototype Python implementation \texttt{DoubleML-Serverless} for the estimation of double machine learning models with the serverless computing platform AWS Lambda and demonstrate its utility with a case study analyzing estimation times and costs.

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Malte S. Kurz. 2021-01-11. Distributed Double Machine Learning with a Serverless Architecture. https://doi.org/10.1145/3447545.3451181

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