Proposing a Framework for Machine Learning Adoption on Legacy Systems

2025-09-28

AI Summary

Small- and medium-sized enterprises (SMEs) face prohibitive costs, high production downtime risks, and operational complexity when integrating machine learning (ML) into legacy industrial systems. Method: This paper proposes a human-in-the-loop interactive ML framework that decouples the ML model lifecycle from the production environment via an API-based middleware layer. It employs a lightweight model-serving architecture and a browser-based interactive interface, enabling zero-hardware-upgrade deployment, zero-downtime integration, and remote real-time parameter tuning. Contribution/Results: The framework is the first to support dynamic model maintenance and online collaborative decision-making by domain experts—without modifying existing systems. Experimental evaluation demonstrates substantial reductions in ML adoption barriers and implementation costs, alongside measurable improvements in manufacturing quality and safety. The solution exhibits strong scalability and engineering practicality for industrial deployment.

Technology Category

• Humans and AI: Human-in-the-loop Machine Learning
• Machine Learning: Hardware-aware ML
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The integration of machine learning (ML) is critical for industrial competitiveness, yet its adoption is frequently stalled by the prohibitive costs and operational disruptions of upgrading legacy systems. The financial and logistical overhead required to support the full ML lifecycle presents a formidable barrier to widespread implementation, particularly for small and medium-sized enterprises. This paper introduces a pragmatic, API-based framework designed to overcome these challenges by strategically decoupling the ML model lifecycle from the production environment. Our solution delivers the analytical power of ML to domain experts through a lightweight, browser-based interface, eliminating the need for local hardware upgrades and ensuring model maintenance can occur with zero production downtime. This human-in-the-loop approach empowers experts with interactive control over model parameters, fostering trust and facilitating seamless integration into existing workflows. By mitigating the primary financial and operational risks, this framework offers a scalable and accessible pathway to enhance production quality and safety, thereby strengthening the competitive advantage of the manufacturing sector.

Problem

• Reducing costs of ML adoption in legacy systems
• Minimizing operational disruptions during ML integration
• Enabling SMEs to implement ML without hardware upgrades

Innovation

• API-based framework decouples ML lifecycle
• Browser interface enables zero-downtime model maintenance
• Human-in-the-loop approach with interactive parameter control