Industry at the Edge posts

Many industrial companies have a data strategy, yet very few can honestly say their business is better because it. The reason often isn’t technology or talent, but a missing piece in what we call the Triangle of Digitalization — a simple framework that reveals why most data strategies quietly fail.

Remote mines can’t depend on the cloud. Edge AI processes data on-site, enabling real-time insights, less downtime, and smarter autonomous operations. It’s becoming the backbone of digital mining.

Discover why Kubernetes struggles at the industrial edge and how edge-native platforms like Barbara deliver resilience, security, and faster ROI.

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.