Industry at the Edge posts

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.

By 2025, a staggering 75% of enterprise data will be created at the edge. Moreover, by 2027, deep learning will be in over 65% of edge use cases. As the volume of data continues to increase, computing is shifting towards the edge. This presents a unique opportunity for AI /ML Teams to learn and adopt best practices in implementing Machine Learning in the Edge. Learn more and replay our webinar on The Cutting Edge of MLOps.

The use of AI in Edge Computing opens up exciting opportunities across industries, offering benefits like real-time decision-making, low latency inferencing, and enhanced data security. However, quantifying these benefits and demonstrating tangible returns on investment remains a challenge for many companies.‍

With the increased number of distributed energy resources DERs onto the grid, energy operators need a predictive system based on consumption and production patterns to help them avoid congestion and overvoltage events in the grid. In this article, we cover a specific project we are running under the i-nergy programme, an EU-funded initiative aiming to support and develop new AI-based Energy Services.