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Crossing the Chasm: Deploying Machine Learning Analytics in Dynamic Real-World Scenarios

Abstract The dawn of Internet of Things (IoT) has opened the opportunity for mainstream adoption of machine learning analytics. However, most research in machine learning has focused on discovery of new algorithms or fine-tuning the performance of existing algorithms. Little exists on the process of taking an algorithm from the lab-environment into the real-world, culminating in sustained value. Real-world applications are typically characterized by dynamic non-stationary systems with requirements around feasibility, stability and maintainability. Not much has been done to establish standards around the unique analytics demands of real-world scenarios.

This research explores the problem of the why so few of the published algorithms enter production... (more)
Created Date 2016
Contributor Shahapurkar, Som (Author) / Liu, Huan (Advisor) / Davulcu, Hasan (Committee member) / Ameresh, Ashish (Committee member) / He, Jingrui (Committee member) / Tuv, Eugene (Committee member) / Arizona State University (Publisher)
Subject Computer science / Statistics / Systems science / Analytics / CRISP-DM / Data Science / Industrial / Machine Learning / Semiconductor
Type Doctoral Dissertation
Extent 181 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis