Industry at the Edge posts

This post explores how Barbara’s MLOps capabilities simplify the remote deployment of AI models — like ResNet18 — to edge devices, all managed effortlessly through a single, intuitive console. Discover how easy edge AI deployment can be.

Our recent webinar with UAO featured a live demonstration of Barbara’s platform in action, showcasing how Schneider’s SoftdPAC runtimes can be deployed across multiple industrial PCs, regardless of the hardware manufacturer, all managed remotely from a centralized platform developed by Barbara.

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.