Towards practicable Machine Learning development using AI Engineering Blueprints

• URL: http://arxiv.org/abs/2504.06391v1
• Date: Tue, 08 Apr 2025 19:28:05 GMT
• Title: Towards practicable Machine Learning development using AI Engineering Blueprints
• Authors: Nicolas Weeger, Annika Stiehl, Jóakim vom Kistowski, Stefan Geißelsöder, Christian Uhl,
• Abstract summary: Small and medium-sized enterprises (SMEs) face challenges when implementing AI in their products or processes.<n>This paper proposes a research plan designed to develop blueprints for the creation of proprietary machine learning (ML) models.
• Score: 0.8654896256058138
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Generative AI Application for Building Industry [10.154329382433213]
  This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs) in the building industry. The research highlights how LLMs can automate labor-intensive processes, significantly improving efficiency, accuracy, and safety in building practices.
  arXiv  Detail & Related papers  (2024-10-01T21:59:08Z)
• Next-Gen Software Engineering. Big Models for AI-Augmented Model-Driven Software Engineering [0.0]
  The paper provides an overview of the current state of AI-augmented software engineering and develops a corresponding taxonomy, AI4SE.<n>A vision of AI-assisted Big Models in SE is put forth, with the aim of capitalising on the advantages inherent to both approaches in the context of software development.
  arXiv  Detail & Related papers  (2024-09-26T16:49:57Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System [85.8338446357469]
  We introduce OmniForce, a human-centered AutoML system that yields both human-assisted ML and ML-assisted human techniques. We show how OmniForce can put an AutoML system into practice and build adaptive AI in open-environment scenarios.
  arXiv  Detail & Related papers  (2023-03-01T13:35:22Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Software engineering for artificial intelligence and machine learning software: A systematic literature review [6.681725960709127]
  This study aims to investigate how software engineering has been applied in the development of AI/ML systems. Main challenges faced by professionals are in areas of testing, AI software quality, and data management.
  arXiv  Detail & Related papers  (2020-11-07T11:06:28Z)
• Validate and Enable Machine Learning in Industrial AI [47.20869253934116]
  Industrial AI promises more efficient future industrial control systems. The Petuum Optimum system is used as an example to showcase the challenges in making and testing AI models.
  arXiv  Detail & Related papers  (2020-10-30T20:33:05Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)
• Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology [53.063411515511056]
  We propose a process model for the development of machine learning applications. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications.
  arXiv  Detail & Related papers  (2020-03-11T08:25:49Z)
• Engineering AI Systems: A Research Agenda [9.84673609667263]
  We provide a conceptualization of the typical evolution patterns that companies experience when employing machine learning. The main contribution of the paper is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions.
  arXiv  Detail & Related papers  (2020-01-16T20:29:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.