Steampunk Machine Learning: Victorian contrivances for modern data science
Abstract
Fitting models to data is all the rage nowadays but has long been an essential skill of engineers. Veterans know that real-world systems foil textbook techniques by interleaving routine operating conditions with bouts of overload and failure; to be practical, a method must model the former without distortion by the latter. Surprisingly effective aid comes from an unlikely quarter: a simple and intuitive model-fitting approach that predates the Babbage Engine. The foundation of industrial-strength decision support and anomaly detection for production datacenters, this approach yields accurate yet intelligible models without hand-holding or fuss. It is easy to practice with modern analytics software and is widely applicable to computing systems and beyond.
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November-December 2021
84 pages
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Association for Computing Machinery
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Published: 18 January 2022
Published in QUEUE Volume 19, Issue 6
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