Jaime Vélez

As artificial intelligence continues to advance, the need for real-time, adaptive, and efficient AI systems becomes increasingly critical. In this article, we dig in into how edge computing complements and enhances adaptive AI, enabling intelligent applications to thrive in diverse and dynamic environments. Join us as we explore the revolutionary synergy between edge computing and adaptive AI.

Edge Computing plays a pivotal role in revolutionizing the marine business, particularly in the domain of energy optimization. By bringing computing resources closer to the data source, edge computing enables real-time data processing and analysis onboard ships and marine installations. This paradigm shift allows for more efficient energy management and optimization, leading to significant cost reductions and a reduced environmental impact.

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 models in edge computing environments

With the rapid growth of artificial intelligence, the environmental impact of AI is a hot topic. Green AI aims to create sustainable, energy-efficient, and environmentally-friendly AI systems. However, achieving this goal requires a combination of different technologies and one of the most critical ones is Edge Computing. In this article, we'll explore Green AI, its importance, and the critical role of Edge Computing in its success.

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.

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.

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.

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 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.

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.