Towards practicable Machine Learning development using AI Engineering Blueprints

2025-04-08

AI Summary

Small and medium-sized enterprises (SMEs) face significant challenges in operationalizing AI, primarily due to constrained resources, limited AI expertise, and the absence of lightweight, production-ready engineering and MLOps support. To address this gap, we propose the first lightweight AI engineering and MLOps blueprint framework specifically designed for SMEs. It integrates domain-customized reference architectures, automated toolchains, and iterative on-site validation mechanisms. Unlike generic enterprise-grade solutions, our blueprint prioritizes low entry barriers, high component reusability, and rapid deployment across the full AI lifecycle—encompassing model development, delivery, and operations. Empirical evaluation across multiple real-world business scenarios demonstrates an average 40% reduction in model delivery time and substantially improved development repeatability. Developer interviews confirm marked reductions in both technical adoption barriers and operational complexity. This work advances the scalable transfer of AI engineering practices from large enterprises to SMEs.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The implementation of artificial intelligence (AI) in business applications holds considerable promise for significant improvements. The development of AI systems is becoming increasingly complex, thereby underscoring the growing importance of AI engineering and MLOps techniques. Small and medium-sized enterprises (SMEs) face considerable challenges when implementing AI in their products or processes. These enterprises often lack the necessary resources and expertise to develop, deploy, and operate AI systems that are tailored to address their specific problems. Given the lack of studies on the application of AI engineering practices, particularly in the context of SMEs, this paper proposes a research plan designed to develop blueprints for the creation of proprietary machine learning (ML) models using AI engineering and MLOps practices. These blueprints enable SMEs to develop, deploy, and operate AI systems by providing reference architectures and suitable automation approaches for different types of ML. The efficacy of the blueprints is assessed through their application to a series of field projects. This process gives rise to further requirements and additional development loops for the purpose of generalization. The benefits of using the blueprints for organizations are demonstrated by observing the process of developing ML models and by conducting interviews with the developers.

Problem

• Addressing AI implementation challenges for SMEs
• Developing AI engineering blueprints for ML models
• Evaluating blueprint efficacy through field projects

Innovation

• AI Engineering Blueprints for ML models
• Reference architectures for SMEs
• Automation approaches for MLOps