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Edge AI, which involves running AI algorithms on local devices rather than in centralized data centers, promises reduced latency, enhanced privacy, and lower bandwidth usage. However, several technical challenges hinder its widespread adoption. This post delves into these challenges, including computational constraints, data management issues and security concerns. Computational Constraints One of the foremost challenges

Artificial Intelligence (AI) has transformed numerous industries by enabling smarter decision-making and automation. Two significant paradigms in this domain are EDGE AI and Distributed AI. While both aim to leverage AI’s capabilities, they operate in distinct manners and offer unique benefits. Understanding their differences and how to integrate EDGE AI into Distributed AI can unlock

In the era of rapidly expanding data and increasing computational demands, the convergence of Edge AI and Distributed AI offers a transformative approach to harnessing the full potential of artificial intelligence. Edge AI, with its ability to process data locally on devices at the edge of the network, complements Distributed AI’s framework of utilizing multiple

Imagine using a smart device, like a smartwatch or a smart security camera. These gadgets are capable of performing remarkable tasks, such as monitoring your heart rate or recognizing faces. The magic behind these abilities is called Artificial Intelligence (AI). Typically, AI processes data by sending it to powerful computers (servers) located far away, a

As we saw in our previous blog posts, running MLOps in a Distributed AI environment has its own challenges. Some of these challenges are inherent to the distributed nature of this implementation, while others manifest due to the specific requirements of running MLOps for Distributed AI. Below is a list of these challenges and how

  In the previous blog post we discussed MLOps for Distributed AI framework. We looked at  how MLOps is different for Distributed AI and what are the changes needed for MLOps to be effective in this emerging field of Machine learning. In this post we see how we can implement these changes. Implementing MLOps for

DevOps is a collaborative approach that merges software development (Dev) and IT operations (Ops), aiming to streamline processes, enhance productivity, and accelerate delivery. By fostering a culture of continuous integration, automated testing, and rapid deployment, DevOps bridges gaps between teams, ensuring efficient, reliable software development and delivery. MLOps: DevOps for AI DevOps practices are increasingly

Artificial Intelligence has taken the world by storm in recent years. Its applications have touched diverse fields such as, predicting how proteins fold from a chain of amino acids, to beating the World Go champion in a series of games, to the ever helpful LLMs like ChatGPT. All these complex AI models need the most

IoT Security and Cybersecurity Companies are, continually striving to make virtual connections everywhere. IoT devices that are connected (smartphones, smart home equipment, etc.) can ‘communicate’ with one another. When there is not enough security, all these connected devices provide direct entry into personal and professional networks and these become vulnerable to theft of data. The

IoT in Manufacturing  With the advent of digital power, the consumer’s approach towards personalization and experience has evolved and this has driven up the popularity of the Internet of Things (IoT).  IoT refers to a network of physical objects that are embedded with software, sensors, and other suitable technologies that help to connect and also