Currently, cloud computing and application programming interfaces (APIs) are used to train and deploy machine learning models. Edge AI then performs machine learning tasks such as predictive analytics, voice recognition, and close-to-user anomaly detection, distinguishing it from common cloud services in a variety of ways. Instead of applications being fully developed and run on the cloud, they edge AI system processes and analyze data close to the points they create.
Machine learning algorithms can be run at the edge, and information can be processed right through IoT devices rather than private data centers or cloud computing capabilities.
Edge AI presents itself as a better option whenever real-time prediction and data processing is required. Consider the latest advances in self-driving vehicle technology. To ensure safe navigation and avoid potential hazards for these vehicles, you need to quickly detect and respond to a variety of factors, including traffic signals, unstable drivers, and lane changes. Additionally, they need to explain pedestrians, curbs and many other variables.
The ability to process this information locally within Edge AI's vehicles reduces the potential risk of connectivity issues that can arise from sending data to remote servers via cloud-based AI. In a scenario of this nature where rapid data responses can determine life and death outcomes, the ability of a vehicle to respond quickly is very important.
Conversely, Cloud AI refers to the deployment of AI algorithms and models on a cloud server. This method provides increased data storage and processing power and facilitates the training and deployment of more advanced AI models.
