Industry at the Edge posts

As industrial operations grow more distributed and data-driven, cybersecurity must keep up. The old model stacking specialized hardware in datacenters, is fast becoming obsolete. In this article, we explore virtualized cybersecurity as a strategic game-changer, especially for sectors like oil & gas, utilities, and manufacturing.

As industries seek faster insights and real-time responsiveness, Edge MLOps (Machine Learning Operations at the Edge) is emerging as a game-changer. Unlike traditional MLOps that rely on centralized cloud infrastructure, Edge MLOps enables machine learning models to be deployed, monitored, and retrained close to the data source.

This article takes you inside a real-world deployment where MLOps at the edge powers hourly solar radiation forecasts in a photovoltaic plant. By combining AI with real-time data flows, and cloud integration, the solution delivers higher prediction accuracy, faster response times, and optimized plant performance. Discover how Edge MLOps is transforming solar forecasting.

The project showcases the benefits of substation virtualization, which enables real-time monitoring, AI-driven fault detection, improved security planning, and cost-effective upgrades. At the core is Barbara’s Edge Node, capable of processing 4,000 signal samples per second and capturing event-triggered data using GOOSE connectors, mirroring the functionality of a physical oscilloscope, remotely and securely. Learn more in the following article.

This post explores how Barbara enables Food & Beverage plants to turn raw data into real-time insights and AI readiness, transforming the Unified Namespace into a real competitive advantage.

Barbara has been a trusted leader in secure orchestration at the Industrial Edge, helping organizations deploy advanced data and AI applications in mission-critical environments. With the launch of Barbara Platform 3.0.0, we’re entering a new chapter,expanding our capabilities to support even greater flexibility, performance, and resilience at scale.