Industry at the Edge posts

According to Gartner, 13% of organizations implementing IoT projects are already leveraging digital twins, while 62% are in the process of planning or developing them. However, industries such as energy, water infrastructure, and process manufacturing encounter significant challenges in adopting digital twin solutions. This article delves into potential strategies to overcome these obstacles.

Edge Computing is becoming a critical component, enabling organizations to leverage edge data in unprecedented ways. However, embarking on an Edge Computing project can be daunting, as building an edge solution requires significant time, investment, and a highly skilled team.This article explores the essentials for getting started with Edge Computing.

View our on-demand webinar "How to Maximize your Edge Data". Whether you're just starting to collect data and want to maximize its potential or you're deep into your digital transformation journey and aiming to harness AI at the edge, this webinar provides actionable insights applicable to every stage of your enterprise’s edge 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.

Deploying machine learning models in various locations is becoming increasingly important for businesses. Whether you're a tech company looking to scale your AI infrastructure or a data scientist deploying models for different clients, understanding the nuances of deploying models in multiple locations is essential. This comprehensive guide will explore the strategies, challenges, and best practices in deploying models across diverse environments.

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.