Industry at the Edge posts

As organizations adopt Edge Computing and Edge AI to power real-time decision-making in industrial environments, traditional tools like Kubernetes fall short. This article explores the evolution from monolithic systems to containerized microservices, the limits of Kubernetes at the edge, and how Barbara’s platform empowers secure and scalable orchestration in distributed, offline, or resource-constrained scenarios.

AI is transforming the way businesses operate, but it also introduces new security concerns. Companies must protect their data from cyberattacks, comply with data protection regulations, and ensure their AI models are ethical and transparent. Deploying AI at the Edge can provide a secure infrastructure for private, compliance, and secure AI deployment.

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?