Industry at the Edge posts

Deploying machine learning models across multiple locations is becoming critical for scaling AI. Whether you're building infrastructure or serving diverse clients, this guide covers key strategies, challenges, and best practices for successful multi-site model deployment.

According to Gartner, 13% of organizations implementing IoT projects are already leveraging digital twins, while another 62% are actively planning or developing them. Yet, industries like energy, water infrastructure, and process manufacturing face significant hurdles in adopting these powerful solutions. This article delves into potential strategies to overcome these challenges.

When I first joined Barbara as People Manager, I was eager to see how the company's values would play out in our daily operations. It’s one thing to talk about culture, but experiencing it firsthand is quite another. This is my experience

The 181 zettabytes of data generated annually by over 13 billion devices exceeds global internet bandwidth, posing challenges for cloud computing due to high costs, latency, and security concerns. Edge computing processes data locally, offering efficient, secure analysis without constant connectivity. Barbara’s Industrial Edge Management and Orchestration Platform simplifies managing this infrastructure by providing cloud-like capabilities on-premises and bridging the IT-OT gap.

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.

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 considerations for technology leaders navigating the Edge AI landscape.