  Advancements in scientific instrument sensors and connected devices provide
unprecedented insight into ongoing experiments and present new opportunities
for control, optimization, and steering. However, the diversity of sensors and
heterogeneity of their data result in make it challenging to fully realize
these new opportunities. Organizing and synthesizing diverse data streams in
near-real-time requires both rich automation and Machine Learning (ML). To
efficiently utilize ML during an experiment, the entire ML lifecycle must be
addressed, including refining experiment configurations, retraining models, and
applying decisions-tasks that require an equally diverse array of computational
resources spanning centralized HPC to the accelerators at the edge. Here we
present the Manufacturing Data and Machine Learning platform (MDML). The MDML
is designed to standardize the research and operational environment for
advanced data analytics and ML-enabled automated process optimization by
providing the cyberinfrastructure to integrate sensor data streams and AI in
cyber-physical systems for in-situ analysis. To achieve this, the MDML provides
a fabric to receive and aggregate IoT data and simultaneously orchestrate
remote computation across the computing continuum. In this paper we describe
the MDML and show how it is used in advanced manufacturing to act on IoT data
and orchestrate distributed ML to guide experiments.
