arXiv subjects

Mikel Egaña Aranguren

Publications and source records attributed to Mikel Egaña Aranguren.

3 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↗

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↗

arXiv2024

The Transparent Relations Ontology (TRO): a vocabulary to describe conflicts of interest

The Transparent Relations Ontology (TRO) offers a vocabulary to publish data about relations between powerful parties that should be more transparent, in order to detect possible conflicts of interest. TRO is based on minimal modelling, reusing common vocabularies to offer a simple yet useful resource to publish interoperable data about pointers to relations that might result in corruption cases. Additionally, best practices have been followed in order to sustain a technically rigorous ontology development process. A usage example with real data is mentioned, integrating information from Basque Government's Open Data services and a news outlet. Building upon its foundational design, future enhancements of TRO could significantly amplify its utility in uncovering and scrutinizing opaque relationships that may lead to corruption.

cs.DL↗