Industry at the Edge posts

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.

In general, it is important to clearly understand your business requirements and the problem you are trying to solve when determining the best approach to automate the retraining of an active machine learning model. It is also important to continuously monitor the performance of the model and make adjustments to the retraining cadence and metrics as needed.

Supported by the Basque Energy Cluster and Innovasturias, the I+CCM project that stands for "Intelligent Engine Control Centre" aims to develop a solution to equip the switchboards that make up an electrical system with intelligence and interaction capacity via Industrial IoT.

In recent years, the debate between Cloud and Edge Computing has been a hot topic in the world of technology. While both technologies have their advantages and disadvantages, it can be challenging to determine which one is the best option for your application. In this article, we will take a closer look at both, their benefits and drawbacks, and ultimately answer the question: Cloud vs Edge, where should I deploy my application?

To operate more efficiently, water management operators face the challenge to monitor in a unified and comprehensive way all components that make up the OT and industrial automation architectures. The use of Edge Nodes allows connectivity with different equipment in each installation regardless of the various data reporting equipment. Discover how utility companies can access sophisticated intelligence to support water operators using Edge Computing.

Barbara joins Sec2Grid Project to accelerate and improve the process of discovery, detection and mitigation of vulnerabilities in grid devices, as a basis for the specification of cybersecurity management requirements in a Smart Grid system.