arXiv subjects

Unai Lopez-Novoa

Publications and source records attributed to Unai Lopez-Novoa.

6 recordsLinked to original sources

arXiv2026

An Empirical Evaluation of Modern MLOps Frameworks

Given the increasing adoption of AI solutions in professional environments, it is necessary for developers to be able to make informed decisions about the current tool landscape. This work empirically evaluates various MLOps (Machine Learning Operations) tools to facilitate the management of the ML model lifecycle: MLflow, Metaflow, Apache Airflow, and Kubeflow Pipelines. The tools are evaluated by assessing the criteria of Ease of installation, Configuration flexibility, Interoperability, Code instrumentation complexity, result interpretability, and Documentation when implementing two common ML scenarios: Digit classifier with MNIST and Sentiment classifier with IMDB and BERT. The evaluation is completed by providing weighted results that lead to practical conclusions on which tools are best suited for different scenarios.

cs.SE↗

arXiv2026

On-Device Deep Reinforcement Learning for Decentralized Task Offloading Performance trade-offs in the training process

Allowing less capable devices to offload computational tasks to more powerful devices or servers enables the development of new applications that may not run correctly on the device itself. Deciding where and why to run each of those applications is a complex task. Therefore, different approaches have been adopted to make offloading decisions. In this work, we propose a decentralized Deep Reinforcement Learning (DRL) agent to address the selection of computing locations. Unlike most existing work, we analyze it in a real testbed composed of various edge devices running the agent to determine where to execute each task. These devices are connected to a Multi-Access Edge Computing (MEC) server and a Cloud server through 5G communications. We evaluate not only the agent's performance in meeting task requirements but also the implications of running this type of agent locally, assessing the trade-offs of training locally versus remotely in terms of latency and energy consumption.

cs.ET↗

arXiv2025

XWAVE: A Novel Software-Defined Everything Approach for the Manufacturing Industry

The manufacturing sector is moving from rigid, hardware-dependent systems toward flexible, software-driven environments. This transformation is shaped by the convergence of several Software-Defined technologies: Software-Defined Automation virtualizes industrial control, replacing proprietary PLCs with containerized, programmable solutions that enable scalability and interoperability. Software-Defined Compute and Communications provide a means to distribute intelligence seamlessly across devices, networks, and cloud platforms, reducing latency and enabling dynamic reconfiguration. Software-Defined Manufacturing Systems, usually implemented as Digital Twins, are real-time virtual models of machines and processes, allowing predictive analysis, optimization, and closer integration between human operators and intelligent systems. This work presents XWAVE, a project that unites these three Software-Defined paradigms to present a modular, fully software-defined manufacturing system.

eess.SY↗

arXiv2025

Using GPT to build a Project Management assistant for Jira environments

In the domain of Project Management, the sheer volume of data is a challenge that project managers continually have to deal with. Effectively steering projects from inception to completion requires handling of diverse information streams, including timelines, budgetary considerations, and task dependencies. To navigate this data-driven landscape with precision and agility, project managers must rely on efficient and sophisticated tools. These tools have become essential, as they enable project managers to streamline communication, optimize resource allocation, and make informed decisions in real-time. However, many of these tools have steep learning curves and require using complex programming languages to retrieve the exact data that project managers need. In this work we present JiraGPT Next, a software that uses the GPT Large Language Model to ease the process by which project managers deal with large amounts of data. It is conceived as an add-on for Jira, one of the most popular Project Management tools, and provides a natural language interface to retrieve information. This work presents the design decisions behind JiraGPT Next and an evaluation of the accuracy of GPT in this context, including the effects of providing different prompts to complete a particular task.

cs.SE↗

arXiv2020

On the performance of a highly-scalable Computational Fluid Dynamics code on AMD, ARM and Intel processors

No area of computing is hungrier for performance than High Performance Computing (HPC), the demands of which continue to be a major driver for processor performance and adoption of accelerators, and also advances in memory, storage, and networking technologies. A key feature of the Intel processor domination of the past decade has been the extensive adoption of GPUs as coprocessors, whilst more recent developments have seen the increased availability of a number of CPU processors, including the novel ARM-based chips. This paper analyses the performance and scalability of a state-of-the-art Computational Fluid Dynamics (CFD) code on three HPC cluster systems equipped with AMD EPYC-Rome (EPYC, 4096 cores), ARM-based Marvell ThunderX2 (TX2, 8192 cores) and Intel Skylake (SKL, 8000 cores) processors. Three benchmark cases are designed with increasing computation-to-communication ratio and numerical complexity, namely lid-driven cavity flow, Taylor-Green vortex and a travelling solitary wave using the level-set method, adopted with $4^{th}$-order central-differences or a $5^{th}$-order WENO scheme. Our results show that the EPYC cluster delivers the best code performance for all the setups under consideration. In the first two benchmarks, the SKL cluster demonstrates faster computing times than the TX2 system, whilst in the solitary wave simulations, the TX2 cluster achieves good scalability and similar performance to the EPYC system, both improving on that obtained with the SKL cluster. These results suggest that while the Intel SKL cores deliver the best strong scalability, the associated cluster performance is lower compared to the EPYC system. The TX2 cluster performance is promising considering its recent addition to the HPC portfolio.

cs.DC↗

arXiv2019

The BIDS Toolbox: A web service to manage brain imaging datasets

Data sharing is a key factor for ensuring reproducibility and transparency of scientific experiments, and neuroimaging is no exception. The vast heterogeneity of data formats and imaging modalities utilised in the field makes it a very challenging problem. In this context, the Brain Imaging Data Structure (BIDS) appears as a solution for organising and describing neuroimaging datasets. Since its publication in 2015, BIDS has gained widespread attention in the field, as it provides a common way to arrange and share multimodal brain images. Although the evident benefits it presents, BIDS has not been widely adopted in the field of MRI yet and we believe that this is due to the lack of a go-to tool to create and managed BIDS datasets. Motivated by this, we present the BIDS Toolbox, a web service to manage brain imaging datasets in BIDS format. Different from other tools, the BIDS Toolbox allows the creation and modification of BIDS-compliant datasets based on MRI data. It provides both a web interface and REST endpoints for its use. In this paper we describe its design and early prototype, and provide a link to the public source code repository.

cs.DL↗