Just as Data Teams embark on their Data Journey, Infrastructure Teams must navigate their own Digital Infrastructure Journey to enable this transformation. This article explores the challenges of IT-OT convergence in industrial companies. Stay tuned as we break down each stage of this evolution and uncover the key steps to a seamless transition.

We've just launched an to Barbara 2.7.5, a version focused entirely on enhancing your user experience and a big step in our journey to make Barbara the most UX-friendly Edge AI platform in the market.

When implementing a data-based strategy, it all starts with the Data. One of the typical sources to extract data are IoT devices. Knowing which IoT communication protocols exist and how to use them for data communication is essential as they are the ones that make data gathering from them possible.

As organizations' appetite for seizing opportunities at the edge grows, data-driven industries must carefully select the right infrastructure to seamlessly scale from initial implementations to large-scale deployments across multiple facilities.This article explores why an Edge Management and Orchestration tool is essential for efficiently and securely scaling edge computing initiatives.

For industrial companies adopting AI, a clear understanding of the MLOps workflow is essential to pinpoint the requirements and tools necessary for successfully bringing use cases into production. In this post, we explore how Barbara seamlessly integrates into the key stages of the workflow that involve the edge.

Edge AI brings machine learning capabilities directly to edge devices, allowing real-time data processing and decision-making at the source without relying on cloud connectivity. This unlocks faster, smarter, and more autonomous operations across the shop floor. In this article we explore the challenges of embracing Edge AI in the Food & Beverage 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.

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 Infrastruicture that deploys and orchestrates Artificial Intelligence at the Edge.

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.