Towards practicable Machine Learning development using AI Engineering Blueprints

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.

💡 Research Summary

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The paper addresses a pressing gap in the adoption of artificial intelligence (AI) and machine learning (ML) by small and medium‑sized enterprises (SMEs). While AI promises substantial business value, SMEs often lack the expertise, resources, and systematic processes required to develop, deploy, and operate proprietary ML models. Existing literature on AI engineering and MLOps provides high‑level principles but rarely offers concrete, SME‑focused artefacts that can be directly applied in practice. To bridge this gap, the authors propose a comprehensive “blueprint” framework that bundles reference architectures, automation pipelines, and tooling recommendations into a reusable package tailored to different AI types (computer vision, time‑series analysis, reinforcement learning, generative AI) and deployment scenarios (cloud, edge, on‑premise).

The blueprint is organized around four inter‑linked sub‑pipelines:

1. Business‑Driver Pipeline – Starts with explicit business requirements and translates them into measurable Objective‑Key‑Results (OKRs) and non‑functional requirements (NFRs). These are then mapped to an architectural design that specifies both software components and ML components, as well as monitoring objectives. This step ensures that AI projects are anchored in concrete business value and that technical decisions are justified against clear metrics.

2. DataOps Pipeline – Handles the entire data lifecycle required for model training. It includes version‑controlled data ingestion, exploratory data analysis, cleaning, validation, feature engineering, and storage in a versioned feature store. By insisting on data versioning at every stage, the pipeline guarantees reproducibility and facilitates data‑quality monitoring. The blueprint provides a toolbox of open‑source and commercial options (e.g., DVC for data versioning, Feast for feature stores) that can be swapped depending on the use case.

3. MLOps Pipeline – Consumes the feature store output to train, validate, and test models. It integrates experiment tracking, hyper‑parameter optimization, and model registry services to produce versioned model artefacts. The blueprint distinguishes between training environments (GPU/TPU clusters, distributed training frameworks) and specifies CI‑style testing of model performance before promotion to production. It also outlines how to handle different AI types – for instance, large‑scale GPU training for computer‑vision models versus streaming data pipelines for time‑series forecasting.

4. DevOps Pipeline – Takes the validated model artefact and packages it for inference. It defines CI/CD processes for containerization (Docker, Kubernetes), API or batch inference interfaces, and deployment strategies aligned with the target environment (cloud, edge devices, or on‑premise servers). Continuous monitoring of prediction accuracy, resource utilization, and data drift is built into the pipeline, with alerting mechanisms for rapid response.

Methodologically, the authors adopt Design‑Science Research (DSR). They first conduct a systematic literature review and semi‑structured interviews with SME stakeholders to identify pain points and requirements. Based on these insights, they design the blueprint artefacts and apply them in a series of real‑world SME projects. Throughout the field trials, they collect quantitative metrics (development cycle time, deployment frequency, model accuracy, operational cost) and qualitative feedback from developers. The results show a roughly 30 % reduction in development time, improved reproducibility, and earlier detection of data‑quality issues compared to ad‑hoc approaches.

During the iterative cycles, additional requirements emerged—such as data privacy compliance, model explainability, and security hardening. The authors responded by extending the blueprint with optional modules (e.g., differential privacy libraries, Explainable AI toolkits, security scanning pipelines). This iterative refinement underscores the blueprint’s extensibility and its capacity to evolve into a more generalized SME‑centric AI engineering framework.

In conclusion, the paper demonstrates that a structured, modular blueprint—grounded in business objectives, rigorous DataOps/MLOps/DevOps practices, and adaptable reference architectures—can substantially lower the barriers for SMEs to adopt AI. By providing concrete artefacts and a repeatable methodology, the work moves AI engineering from a theoretical concept toward a practical, scalable solution for organizations with limited resources. Future work is suggested to broaden the validation across more industries, increase automation (e.g., auto‑generated pipelines), and explore a full “AI‑as‑a‑Service” offering built on the proposed blueprints.