Distributed Double Machine Learning with a Serverless Architecture

Serverless cloud computing is predicted to be the dominating and default architecture of cloud computing in the coming decade (Berkley View on Serverless Computing, 2019). In this paper we explore serverless cloud computing for double machine learning. Being based on repeated cross-fitting, double machine learning is particularly well suited to exploit the enormous elasticity of serverless computing. It allows to get fast on-demand estimations without additional cloud maintenance effort. We provide a prototype implementation DoubleML-Serverless written in Python that implements the estimation of double machine learning models with the serverless computing platform AWS Lambda and demonstrate its utility with a case study.