  Recently, there has been significant interest in applying machine learning
(ML) techniques to X-ray scattering experiments, which proves to be a valuable
tool for enhancing research that involves large or rapidly generated datasets.
ML allows for the automated interpretation of experimental results,
particularly those obtained from synchrotron or neutron facilities. The speed
at which ML models can process data presents an important opportunity to
establish a closed-loop feedback system, enabling real-time decision-making
based on online data analysis. In this study, we describe the incorporation of
ML into a closed-loop workflow for X-ray reflectometry (XRR), using the growth
of organic thin films as an example. Our focus lies on the beamline integration
of ML-based online data analysis and closed-loop feedback. We present solutions
that provide an elementary data analysis in real time during the experiment
without introducing the additional software dependencies in the beamline
control software environment. Our data demonstrates the accuracy and robustness
of ML methods for analyzing XRR curves and Bragg reflections and its autonomous
control over a vacuum deposition setup.
