A Modular Engineering Framework for Deploying Artificial Intelligence Systems in Industrial Applications
Keywords:
industrial AI, modular engineering, MLOps, system architecture, governance, deployment framework, socio-technical systemsAbstract
The adoption of artificial intelligence in industrial environments promises transformative improvements in operational efficiency, predictive maintenance, quality control, and autonomous decision-making. Yet translating laboratory-grade models into robust, trustworthy, and continuously evolving production systems remains a formidable challenge. This paper presents a modular engineering framework designed to address the systemic complexity of deploying AI in industrial applications. The framework is grounded in the principles of loose coupling, encapsulation, and standardized interfaces, enabling independent evolution of data pipelines, feature engineering, model training, deployment orchestration, monitoring, and governance components. By drawing on mature software architecture paradigms and integrating them with the unique lifecycle demands of machine learning systems, the proposed approach allows organizations to manage technical debt, enforce consistent governance, and maintain operational resilience across heterogeneous industrial landscapes. The discussion systematically examines architectural trade-offs, the role of containerization and event-driven microservices, the integration of fairness and explainability constraints, and the continuous validation mechanisms required to sustain performance in the face of data drift and evolving regulatory requirements. Crucially, the framework emphasizes socio-technical alignment, recognizing that modularity must extend beyond code into team structures, accountability boundaries, and policy compliance. Through deep conceptual analysis and comparisons across manufacturing, energy, and logistics domains, the paper articulates how modular decomposition mitigates the brittleness often observed in monolithic AI deployments. The framework further addresses sustainability considerations, including energy-aware model serving and the repurposing of modules across use cases. The conclusion outlines a research agenda for empirical validation and the development of open reference architectures that can accelerate industrial AI maturity while preserving safety, fairness, and long-term maintainability.
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