Technology 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.

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.

Organizations across all industries are rapidly deploying edge computing to address challenges related to latency, cost of bandwidth, data sovereignty and control, or business continuity. Industry edge solutions address a variety of business outcomes through different use cases enabling digital transformation across industries. After initial deployments, organizations are starting to ask for vertically integrated edge computing solutions and packages more tailored to their specific needs according to the Gartner® Hype Cycle™ report for Edge Computing.

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.

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.

One of the most transformative technologies that have emerged in recent years is software containerization, which revolutionizes the way industrial companies develop, deploy, and manage their software solutions. Kubernetes is very useful for large, cloud-dependent workloads like web applications, but it is not optimal for smaller, more industrial-oriented workloads that must operate in complex, isolated environments.‍ In this article we explain why.