  Machine learning (ML) has become an integral component of high energy physics
data analyses and is likely to continue to grow in prevalence. Physicists are
incorporating ML into many aspects of analysis, from using boosted decision
trees to classify particle jets to using unsupervised learning to search for
physics beyond the Standard Model. Since ML methods have become so widespread
in analysis and these analyses need to be scaled up for HL-LHC data, neatly
integrating ML training and inference into scalable analysis workflows will
improve the user experience of analysis in the HL-LHC era. We present the
integration of ML training and inference into the IRIS-HEP Analysis Grand
Challenge (AGC) pipeline to provide an example of how this integration can look
like in a realistic analysis environment. We also utilize Open Data to ensure
the project's reach to the broader community. Different approaches for
performing ML inference at analysis facilities are investigated and compared,
including performing inference through external servers. Since ML techniques
are applied for many different types of tasks in physics analyses, we showcase
options for ML integration that can be applied to various inference needs.
