Phd. Ana I Torre-Bastida

In recent years, more and more devices are connected to the network, generating an overwhelming amount of data. This term that is booming today is known as the Internet of Things. In order to deal with these data close to the source, the term Edge Computing arises. The main objective is to address the limitations of cloud processing and satisfy the growing demand for applications and services that require low latency, greater efficiency and real-time response capabilities. Furthermore, it is essential to underscore the intrinsic connection between artificial intelligence and edge computing within the context of our study. This integral relationship not only addresses the challenges posed by data proliferation but also propels a transformative wave of innovation, shaping a new era of data processing capabilities at the network’s edge. Edge devices can perform real-time data analysis and make autonomous decisions without relying on constant connectivity to the cloud. This article aims at analysing and comparing Edge Computing devices when artificial intelligence algorithms are deployed on them. To this end, a detailed experiment involving various edge devices, models and metrics is conducted. In addition, we will observe how artificial intelligence accelerators such as Tensor Processing Unit behave. This analysis seeks to respond to the choice of a device that best suits the necessary AI requirements. As a summary, in general terms, the Jetson Nano provides the best performance when only CPU is used. Nevertheless the utilisation of a TPU drastically enhances the results.

Data science projects represent a greater challenge than software engineering for organizations pursuing their adoption. The diverse stakeholders involved emphasize the need for a collaborative culture in organizations. This article aims to offer joint insights into the role of MLOps and AIOps methodologies for raising the success of data science projects in various fields, ranging from pure research to more traditional industries. We analyze the open issues, opportunities, and future trends organizations face when implementing MLOps and AIOps. Then, the frameworks and architectures that promote these paradigms are presented, as are the different fields in which they are being utilized. This systematic review was conducted using an automated procedure that identified 44,903 records, which were filtered down to 93 studies. These articles are meant to better clarify the problem at hand and highlight the future areas in both research and industry in which MLOPs and AIOps are thriving. Our findings indicate that AIOps flourish in challenging circumstances like those presented by 5G and 6G technologies, whereas MLOps is more prevalent in traditional industrial environments. The use of AIOps in certain stages of the ML lifecycle, such as deployment, remains underrepresented in scientific literature.