Distributed Double Machine Learning with a Serverless Architecture
This paper addresses the problem of efficiently and cost-effectively estimating double machine learning models for researchers and practitioners by leveraging serverless architectures.
This paper explores serverless cloud computing for double machine learning, which is well-suited for parallelization due to its repeated cross-fitting nature. The authors provide a Python implementation, DoubleML-Serverless, for AWS Lambda and demonstrate its utility by analyzing estimation times and costs.
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.