Distributed Double Machine Learning with a Serverless Architecture

• URL: http://arxiv.org/abs/2101.04025v2
• Date: Wed, 24 Feb 2021 12:13:03 GMT
• Title: Distributed Double Machine Learning with a Serverless Architecture
• Authors: Malte S. Kurz
• Abstract summary: This paper explores serverless cloud computing for double machine learning. Double machine learning is particularly well suited to exploit the high level of parallelism achievable with serverless computing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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