  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.
