In this paper, we discuss the design considerations and challenges of using applied machine learning in complex systems, a necessity of operationalizing machine learning techniques. Although many applications of machine learning intend to discern key information insights from large collections of data, in realizable systems the quantity of insights may be so numerous that the insights remain as data and encumber a system and its users. New system design principles are emerging as a result of the dynamism of the machine learning community.
Travis W. Axtell, Lucas A. Overbey, and Lisa Woerner, "Machine learning in complex systems," Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350B (Presented at SPIE Defense + Security: April 16, 2018; Published: 4 May 2018); https://doi.org/10.1117/12.2309547.
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