From Prototype to Production: Common ML Pitfalls and Solutions for Monitoring Infrastructure Assets

Machine learning is rapidly moving from lab prototypes and synthetic environments to field-deployed systems, yet several of these initiatives stall when confronted with real-world data and operating conditions. We synthesize six recurring pitfalls tha

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Publication Details

Published
2025-08-25
DOI
https://doi.org/10.36227/techrxiv.175615738.89418334/v1
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
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article

From Prototype to Production: Common ML Pitfalls and Solutions for Monitoring Infrastructure Assets

Aynan Kareem
Industrial Vision Systems and Defect Detection
article

From Prototype to Production: Common ML Pitfalls and Solutions for Monitoring Infrastructure Assets

Aynan Kareem
article en

Abstract

Machine learning is rapidly moving from lab prototypes and synthetic environments to field-deployed systems, yet several of these initiatives stall when confronted with real-world data and operating conditions. We synthesize six recurring pitfalls tha

University of California System (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 69%
Industrial Vision Systems and Defect Detection
DOIOpen Access PDFOpenAlex
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From Prototype to Production: Common ML Pitfalls and Solutions for Monitoring Infrastructure Assets — Aynan Kareem · (2025) | TGRS Research Map | TGRS