Miren Zabaleta

The food and beverage industry stands on the brink of a new era, driven by the transformative power of Artificial Intelligence in the Edge. By processing data on-site, businesses can immediately adjust operations, predict maintenance issues, and ensure product quality, directly impacting their bottom line. In this article we explore the challenges of embracing Edge AI in the Food Industry.

The integration of Artificial Intelligence (AI) in automotive manufacturing is not a new concept. However, the shift towards the Edge where AI algorithms operate on the data generated at the source rather than being sent to a centralised server, is a game-changer. In this article we explore the main challenges of embracing Edge AI and why moving AI to the edge brings unprecedented levels of efficiency, safety, and sustainability to car manufacturers.

Many companies find themselves underprepared for the complexities involved in expanding their projects within the Edge. Proof of concepts (POCs) typically focus on one or a few locations, but if successful, they must scale to hundreds or even thousands of locations. This article highlights key factors for thriving with Edge AI in the digital age.

AI enables machines to learn from data, make decisions, and adapt to changing conditions, thereby optimising manufacturing processes to a greater extent. This fusion of automation and AI is transforming the manufacturing industry and driving innovation in ways never seen before.

In industrial manufacturing, the cement industry is notable for its considerable environmental impact and high energy usage. Amid increasing environmental concerns and a drive towards sustainability, edge computing presents innovative solutions to improve supply chain efficiency, sustainability, energy conservation, and product traceability.

In the age of AI, the chemical industry finds itself on the brink of a major shift, propelled by the demands for enhanced efficiency, sustainability, and innovation.‍ Edge AI emerges as a key technological enabler, offering unparalleled capabilities for real-time monitoring and control, predictive maintenance, supply chain management, and enhancing sustainability and energy waste optimization.

By implementing real-time optimized Machine Learning algorithms at each of its desalination plants, ACCIONA a global infrastructure operator, managed to minimize the use of reactive chemicals, eliminate associated regulatory penalties, and leverage an edge infrastructure to implement new predictive applications for water quality control. In this article, we explore the intricacies of the project.

Barbara and Gridfy have joined forces to bring AI Flexibility algorithms to the Edge.‍‍ Gridfy has developed and implemented the AI flexibility algorithm located in one of Cuerva´s substations while Barbara has provided the Edge Platform that deploys and orchestrates Artificial Intelligence in the Edge.

We are back at ENLIT Europe 2023, to showcase how Transmission and Distribution System operators can improve their operations by virtualizing HV, MV and LV substations and add intelligence to their assets with Edge AI Technology.

By 2025, a staggering 75% of enterprise data will be created at the edge. Moreover, by 2027, deep learning will be in over 65% of edge use cases. As the volume of data continues to increase, computing is shifting towards the edge. This presents a unique opportunity for AI /ML Teams to learn and adopt best practices in implementing Machine Learning in the Edge. Learn more and replay our webinar on The Cutting Edge of MLOps.

Companies are increasingly interested in virtualizing electrical equipment because it offers a range of benefits for their operations. Virtualization reduces the need for physical infrastructure, which can result in cost savings, reduced maintenance requirements, and increased safety. We will be at CIRED 2023, showcasing how to virtualize substations from Transmission to Middle and Low Voltage networks through Edge Computing Technology.

With the increased number of distributed energy resources DERs onto the grid, energy operators need a predictive system based on consumption and production patterns to help them avoid congestion and overvoltage events in the grid. In this article, we cover a specific project we are running under the i-nergy programme, an EU-funded initiative aiming to support and develop new AI-based Energy Services.