Abstract The rapid integration of Machine Learning (ML) in organizational practices has driven demand for substantial computational resources, incurring both high economic costs and environmental impact, particularly from energy consumption. This challenge is amplified in dynamic data environments, where ML models must be frequently retrained to adapt to evolving data patterns. To address this, more […]

Abstract The technological evolution carried out in recent years has enabled significant developments in various areas of Artificial Intelligence (AI), such as Generative AI. Large Language Models (LLMs) are becoming increasingly complex, allowing for better results and enhancing their real-world applicability. However, these models still face issues such as hallucination or outdated information. This last […]

Abstract Many of today’s domains of application of Machine Learning (ML) are dynamic in the sense that data and their patterns change over time. This has a significant impact in the ML lifecycle and operations, requiring frequent model (re-)training, or other strategies to deal with outdated models and data. This need for dynamic and responsive […]

Abstract Globally, sustainability concerns have been driving several green strategies towards new business models for industry circularity. While industrial greenality is broadly discussed in literature, still missing a clear roadmap tailored for small and medium enterprises (SMEs) to translate circularity practices into tangible, value-driven datasets. This research work addresses this existing gap by proposing a […]