Towards practicable Machine Learning development using AI Engineering Blueprints

TL;DR

The paper addresses the practical gap in guiding SMEs to develop and operate AI systems by proposing AI engineering blueprints. It introduces a guiding four-pipeline blueprint (Business driver, DataOps, MLOps, DevOps) and a Design Science Research method to build, validate, and generalize reference architectures and automation across AI types and deployment scenarios. Field projects and stakeholder interviews are used to evaluate effectiveness and drive iterative improvements, with the aim of streamlining end-to-end AI development for SMEs. The contributions promise concrete, reusable artifacts to lower resource barriers and accelerate enterprise-scale AI adoption for small and medium enterprises.

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.

Figures (5)

• Figure 1: Guiding pipeline with four sub-pipelines.
• Figure 2: Business driver pipeline for defining requirements, architecture and objectives.
• Figure 3: DataOps pipeline to prepare the data for model training.
• Figure 4: MLOps pipeline for model training, validation and versioning.
• Figure 5: DevOps pipeline for system integration of the model artifact.