Democratisation of AI with Understandable and Easily Accessible Machine Learning Operations

While Machine Learning Operations are essential for modern industrial systems, their complexity often excludes domain experts who lack profound AI expertise. In this paper, the DeKIOps research project addresses this gap by positioning UI & UX design as the primary catalyst for the democratisation of AI. Building on prior work in optimisation of energy usage of industrial multi-sensor platforms, this research defines a clear framework to empower non-experts to manage complex Machine Learning systems autonomously. The core contribution is an interdisciplinary framework that translates Guidelines for Human-AI Interaction as established by Amershi et al. along with findings from expert interviews into actionable UI & UX principles for Machine Learning Operations. We evaluated these guidelines through a mixed-methods study using a functional prototype designed for autonomous model management including automated retraining, data augmentation and multi-layered explainable visualisations. Our results yielded a System Usability Scale score of 84.5, which translates to a grade of A+, demonstrating that the framework effectively enables laymen to steer complex Machine Learning systems and achieve operational independence, fostering true AI democratisation.