Abstract The advent of large Machine Learning models and the steep increase in the demand for AI solutions occurs at the same point in time in which policies are being enacted to implement more sustainable processes in virtually every sector. This means there is a need for more, better and larger models, which require significant […]

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 However, these models still face issues such as hallucination or outdated information.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impact in the ML lifecycle and operations, requiring frequent model (re-)training, or other strategies to deal with outdated models and data.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 […]