The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard machine learning operations (MLOps) tools and platforms and the unique requirements of modern and Open Science, particularly regarding the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This paper presents AI4EOSC, a federated, open-source platform designed to operationalize the full AI/ML life-cycle within the European Open Science Cloud (EOSC) ecosystem. Our methodology tackles the fragmentation of distributed research infrastructures by integrating a modular and distributed architecture comprising an AI development platform, a serverless AI-as-a-Service layer, and a federated orchestration model that is able to integrate heterogeneous computing and storage resources from distributed e-infrastructures. AI4EOSC also introduces a “FAIR-by-design” approach that enforces metadata standardization (via MLDCAT-AP) and W3C PROV-compliant provenance tracking through a platform-integrated CI/CD pipeline. The added value of AI4EOSC is demonstrated through the delivery of a diverse set of community installations, which show consistent and seamless deployment across heterogeneous cloud providers. These installations are validated by a set of scientific cases, showing how our work reduces the manual burden on researchers while ensuring high levels of reproducibility and interoperability and providing a unified environment for the development, training, and production of AI/ML models in the EOSC.

The lack of clarity about the definition of Computational Thinking (CT) undermines its assessment and the formulation of effective learning strategies for its development. We propose an assessment tool in Spanish, designed by carefully selecting Bebras tasks, to measure four specific CT skills in first-year university students with no prior programming experience. The test was validated with a sample of 980 students from a Latin American university. We found a high, positive correlation between test results and Math test results from the National University Selection System, and a medium, positive correlation between test results and grades from the midterm exam in a Programming course. An acceptable level of internal consistency was found (Cronbach's alpha=0.70). We examined the validity of the test using Classical Test Theory. One question showed poor discrimination potential. Its elimination from the test increased the internal consistency, so we propose to replace it.

An adaptive system (AS) evaluates its own behavior and changes it when the evaluation indicates that the system is not accomplishing what it is intended to do, or when better functionality or performance is possible. MAPE-K is a reference model that prescribes the adaptation mechanism of ASs by means of high-level abstractions such as Monitors, Analyzers, Planners and Executors and the relationships among them. Since the abstractions and the relationships provided by MAPE-K are generic, other reference models were proposed focusing on providing lower level abstractions to support software engineers in a more suitable way. However, after the analysis of seven representative ASs, we realized the abstractions prescribed by the existing reference models are not properly implemented, thus leading to architectural drifts. Therefore, in this paper we characterized three of these drifts by describing them with a template and showing practical examples. The three architectural drifts of ASs are Scattered Reference Inputs, Mixed Executors and Effectors, and Obscure Alternatives. We expect that by identifying and characterizing these drifts, we can help software architects improve their design and, as a consequence, increase the reliability of this type of systems.