Industry at the Edge posts

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.

In today's fast-paced and competitive landscape, optimizing operations is crucial for success. With the advent of cutting-edge technologies like Edge Computer Vision, businesses can gain a significant advantage by leveraging real-time data analysis and decision-making. In this article, we will explore what industries need to know about optimizing operations with Edge Computer Vision and how this transformative technology can propel their growth.

Artificial Intelligence (AI) is revolutionizing all industries, providing new opportunities and challenges for growth and innovation. However, with great power comes greater responsibility. The European Union (EU) has recognized the urgent need for ethical and transparent AI practices to protect individuals' rights and to ensure fair and accountable use of AI technologies. This article aims to guide companies on what they must do to comply with EU AI regulations.

‍‍In today's fast-paced business landscape, artificial intelligence (AI) and machine learning (ML) have become instrumental in many business processes. MLOps is a rapidly growing field that is revolutionizing the way Machine Learning models are being deployed and managed. By using MLOps in the Edge, organizations can take advantage of the benefits of local processing, increased security and privacy, and reduced bandwidth usage. This article delves into the advantages and challenges of deploying ML in the Edge.