AI empowers machines to learn from data, make smarter decisions, and adapt in real time, driving unprecedented efficiency in manufacturing. The convergence of AI and automation is reshaping the industry and accelerating innovation. In this article, we explore how organizations can successfully embark on their Edge AI journey.

Edge Computing emerges as a transformative technology in the oil and gas industry, driving efficiency and innovation in digital transformation efforts. By harnessing the power of edge computing, organizations can optimize operations, enhance safety protocols, and extract greater value from their resources.

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 embracing AI, understanding the MLOps workflow is key to turning use cases into real-world results. In this post, we show how Barbara integrates seamlessly into each stage of the workflow especially where the edge plays a critical role.

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.