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Edge Artificial Intelligence (AI) software is a combination of edge computing technology and machine learning. This AI software automatically transforms raw data into movements or events, predictions, insights without costly analysis of data and big data. The software tools that are offered are in the form of frameworks and software development kit which offers benefit of merging AI abilities with software applications. In this edge AI software, an AI algorithm is processed on the hardware devices with the help of sensors and signals. With the adoption of this system, there are lesser chances of remaining connected in a live manner, as the system senses the connection and makes the decision dynamically.
Increasing enterprise workloads on cloud, rapid developments of IoT, and growth in number of intelligent applications are the major driving factors for the growth of edge AI software market. However, security concerns can hamper the growth of market. However, emergence of 5G network is anticipated to bring IT and telecom and thereby creating opportunistic factor for global market.
Edge computing makes it possible for AI technologies that are typically serviced by a SaaS offering to work offline, or in situations with low latency or low bandwidth requirements. With edge AI, companies can deploy their machine learning models to run locally on edge devices, helping to counteract issues with both performance and latency. AI systems can deliver real-time feedback to enhance mission-critical applications. In the short term, edge AI will most likely benefit industrial-heavy applications such as manufacturing and supply chain. The investment required to implement edge AI systems is still high, so most industry leaders will need to see a clear ROI to justify the decision.
In the Industrial Internet of Things (IoT), that ROI could be more. For example, in manufacturing, you could use edge AI to enable predictive maintenance, troubleshooting and anticipating issues within a complex physical system. IoT sensors could manage and optimize the supply chain. Edge AI could even be used to automate product testing and inspection, increasing quality while reducing resource expenditure.
The Internet of Things and corporate Bring Your Own Device (BYOD) policies have created opportunities for improvements in efficiency and productivity as employees use the devices that they are most familiar with to do their jobs. However, security teams and policies have not caught up with the new rules. Around 66% of IT professional’s lack of how many devices employees bring into work, while an estimated 84% of companies have experienced an IoT-related breach. The most common security vulnerabilities associated with IoT devices, which pose a serious threat with the increased computational power that comes with edge computing.
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