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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 anarXiv:2205.01476v1 fatcat:7na6vbcoibgmvn6fnhtfb6s2fe