Industry at the Edge posts

Edge Computing helps in integrating Information, Operation, and Engineering technologies since it converts data into valid information for real-time decision-making and can relate data coming from IT, OT, and ET systems, which can be difficult to integrate due to their very different origins. In this article written by Daniel Garrote, we dig into the different systems and the role of Edge Computing in helping integrate all these systems.

As artificial intelligence continues to advance, the need for real-time, adaptive, and efficient AI systems becomes increasingly critical. In this article, we dig in into how edge computing complements and enhances adaptive AI, enabling intelligent applications to thrive in diverse and dynamic environments. Join us as we explore the revolutionary synergy between edge computing and adaptive AI.

Edge Computing plays a pivotal role in revolutionizing the marine business, particularly in the domain of energy optimization. By bringing computing resources closer to the data source, edge computing enables real-time data processing and analysis onboard ships and marine installations. This paradigm shift allows for more efficient energy management and optimization, leading to significant cost reductions and a reduced environmental impact.

Deploying computer vision models in production is a complex endeavour that requires a holistic approach that encompasses data, models, infrastructure, and processes. By addressing the challenges of data acquisition, model selection, infrastructure, CI/CD, monitoring, and ethical considerations, organizations can successfully deploy computer vision models at scale. Thibaut Lucas, CEO and Co-founder at Picsellia shares his view on both, the business and technical aspects surrounding the challenges of deploying Computer Vision at scale.

As thousands of millions of assets are connected to the Internet the industrial world faces new challenges when it comes to connectivity and cybersecurity for real-time automation and decision making . In this talk we address the key role of an Edge Platform as a solution to overcome the Data Gravity issue of the Cloud.

TinyML has proven to be a powerful tool for implementing machine learning models in devices and environments with limited resources. In this article, we explore its potential in the refinery and chemical sector.