Jaime Vélez

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.

Artificial Intelligence and Machine Learning are the most popular technologies used to create intelligent systems and although they are related, they are not the same. Because of this relationship, when you look at Artificial Intelligence versus Machine Learning, you are actually analyzing their interconnectedness.

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.

The digitization of industry requires interoperability for data sharing and process automation. We delve into how Edge Computing plays a critical role in achieving interoperability by executing data processing at the source, rather than sending it to a centralized location, and at the same time reducing latency, increasing security, and improving the scalability of the system.

Although much of the water infrastructures in place are dozens of years old, in recent times their processes have changed radically thanks to a large extent, to digital technology. Edge computing is one of those technologies that will change the shape of the water industry due to the speed and reliability it provides when modernizing this type of infrastructure.

The rise of Industry 4.0, has brought a sheer amount of data. With the advent of IoT, automation, and advanced analytics, organizations are collecting and generating more data than ever. This data has the potential to revolutionize the way industries operate and improve decision-making. However, with the vast amount of data being generated, its crucial that organizations have the ability to effectively control and manage it.

A new concept resonates in recent times among analysts and professionals in the energy sector: the Internet of Energy. It is a trend that has arisen from the urgency caused by the accumulation of events affecting a sector that is more in crisis than ever. Factors such as climate change, international conflicts, the supply crisis, escalating prices and new regulatory requirements have put the sector in the spotlight.

The purpose of this article is to help the industrial ecosystem (service providers, integrators,, companies...) to better understand the industrial landscape of Edge technology. It is known that by 2026, 30% of packaged enterprise capabilities will be deployed on the Edge with integrated resources (gateways with local compute) to drive inference and business outcomes on nearby data. Find out more on the survey we conducted over 200 industrial companies.

The digital revolution is accelerating industrial transformation. Ubiquitous innovation, global markets, and increasingly demanding customers who no longer seek just a product, but a comprehensive service tailored to their needs are forcing manufacturers to transform their business models and move from one-off sales to one of higher value-added services.

Edge computing plays a key role in sustainability and energy efficiency because it is specifically designed to bring applications and data closer to devices and their users. Increasing the deployment of edge solutions could be one of the answers to achieve the energy optimization goal that the industry is looking for.

These are the conclusions reached by Barbara, which presents research on cybersecurity in this sector. Forty percent of industrial organizations have already experienced at least one security incident in the last year, with the industrial and energy sectors being the most vulnerable.

The transition from a business model of selling products to a model of selling services associated with them is occurring in all sectors. Solar energy equipment manufacturers can now jump on the servitization bandwagon and move from a solar tracker sales and implementation model to a pay-per-use model, through the constant collection and processing of data on the status of the photovoltaic plant.