Artificial intelligence technologies include machine learning, predictive analytics, and neural networks. Data collected from the edge device is tagged and then the data engineer prepares the pipe to enter it into the data model. These engineers have the expertise to create software solutions around big data. Data scientists who specialize in programming languages such as mathematics, statistics, and C and C++ create artificial intelligence models using machine learning algorithms that are fine-tuned for various known applications. These models are ultimately presented in different forms such as neural networks, decision trees, or inference rule sets.
Machine learning is divided into supervised learning and unsupervised learning. Unsupervised learning (only input variables, no corresponding output variables) can help developers interpret data more thoroughly, and supervised learning is the foundation of most practical machine learning. In the training phase of supervising machine learning, a large number of data streams need to be mined to extract useful patterns or inferences from multiple calculations to make predictions.
In the application phase of artificial intelligence, the data collected from the edge device can be input from the model selected from the available data models through a standard framework such as Tensorflow. The modeling process requires considerable data processing power, and this is usually the case with core node locations such as cloud sites and large data centers.
In the deployment phase, everything started to get interesting. For example, an edge device can access a package associated with a selected model from a shared repository without having to rely too much on the cloud. In areas such as health monitoring, edge computing can benefit a lot of wearable devices that require unsupervised machine learning for users. In addition, custom applications that require rapid data processing without the need for prior learning often require extremely high levels of data processing power, which is the specialty of edge artificial intelligence.
In most cases, due to technical or energy constraints, data cannot be transmitted to the cloud where artificial intelligence is located. Applications such as voice or video recognition require immediate identification and inference, and no communication delays. In some cases, deployments do not provide a stable connection, so an extensible hybrid architecture is needed to build the required models on the cloud but the inference tasks are executed at the edge. This method only needs to transfer a small amount of data to the core node location, which can optimize bandwidth efficiency, reduce latency and improve response speed.