  Imaging, scattering, and spectroscopy are fundamental in understanding and
discovering new functional materials. Contemporary innovations in automation
and experimental techniques have led to these measurements being performed much
faster and with higher resolution, thus producing vast amounts of data for
analysis. These innovations are particularly pronounced at user facilities and
synchrotron light sources. Machine learning (ML) methods are regularly
developed to process and interpret large datasets in real-time with
measurements. However, there remain conceptual barriers to entry for the
facility general user community, whom often lack expertise in ML, and technical
barriers for deploying ML models. Herein, we demonstrate a variety of
archetypal ML models for on-the-fly analysis at multiple beamlines at the
National Synchrotron Light Source II (NSLS-II). We describe these examples
instructively, with a focus on integrating the models into existing
experimental workflows, such that the reader can easily include their own ML
techniques into experiments at NSLS-II or facilities with a common
infrastructure. The framework presented here shows how with little effort,
diverse ML models operate in conjunction with feedback loops via integration
into the existing Bluesky Suite for experimental orchestration and data
management.
