Industry at the Edge posts

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.

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.

We have seen especially during the last few months how model releases with billions of parameters requiring high processing power have been reproduced. On the other hand, there is also a growing trend that revolves around the ability to run lightweight models in real-time without the need for constant connection on low-power devices such as microcontrollers, sensors, and other embedded systems which is also revolutionizing the AI industry. This trend is known as TinyML.

Edge Computing enables industrial organizations to make decisions and take action in real-time, reduce latency, improve reliability, enhance security, reduce costs and enable remote monitoring and control. In this article, we explore how Edge Computing is becoming a reference technology for industrial companies that seek to digitize their operations.