Data science projects represent a greater challenge than software engineering for
organizations pursuing their adoption. The diverse stakeholders involved emphasize the …

At the confluence of two great paradigms such as Edge Computing and Artificial Intelligence,
Edge Intelligence arises. This new concept is about the smart exploitation of Edge …

Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-
Cycle (SDLC). As the prevalence of software systems increases and becomes more …

The evolution towards more human-centered 6G networks requires the extension of network
functionalities with advanced, pervasive automation features. In this direction, cloud-native …

With the growing popularity of machine learning, implementations of the environment for
develo** and maintaining these models, called MLOps, are becoming more common. The …

Over the last few years, DevOps methodologies have promoted a more streamlined
operationalization of software components in production environments. Infrastructure as …

While many companies aim to use Machine Learning (ML) models, transitioning to
deployment and practical application of such models can be very time-consuming and …

In the Industry 4.0 scene, Artificial Intelligence (AI) is sought after as a new way of getting a
competitive advantage from other market competitors. This technology can support not only …

Develo** and implementing the machine learning applications as quickly as feasible is
the aim of commercial machine learning (ML) initiatives. A lot of machine learning trials …

This article addresses the challenge of reproducing machine learning (ML) experiments by
integrating processes based on MLOps and semantic technologies. The inherent complexity …