The language of edge computing

Artificial intelligence (AI) is a field of computer science that focuses on creating machines that can think, learn, and act like humans. AI uses algorithms to process and analyze large amounts of data, and then uses that information to make decisions, predictions, and recommendations. AI can be used to automate tasks, improve efficiency, and provide insights that would be difficult or impossible for humans to uncover on their own.

Edge AI is the practice of deploying artificial intelligence (AI) algorithms and models on edge devices such as smartphones, IoT sensors, and other devices with limited computing power, instead of relying on cloud servers. The purpose of edge AI is to process and analyze data in real-time, closer to the source, which results in increased speed, enhanced security, and decreased costs.

Edge analytics is the process of analyzing data at the edge of a network, closer to where the data is generated, rather than sending it to a centralized server or cloud for analysis. This approach allows for real-time analysis and faster decision-making, as well as reducing the amount of data that needs to be transmitted over the network.

Edge cloud, also known as the edge cloud computing or edge computing cloud, is a distributed computing infrastructure that brings cloud computing capabilities closer to the edge of the network. It is designed to process and store data locally on devices or edge servers, rather than sending it to a centralized cloud data center for processing.

Edge computing is a technology that allows processing and storage of data on devices located closer to the source of data instead of sending it to a centralized server. In simpler terms, it brings the computing power closer to where the data is generated. This can include devices such as smartphones, sensors, or other internet-connected devices.

An edge network is a decentralized computer network that is designed to bring data processing and storage closer to where it's needed, such as the end user, rather than in a centralized location like a cloud data center. This enables faster data processing and reduced latency, which is crucial for real-time applications like video streaming, online gaming, and autonomous vehicles.

Predictive maintenance is a type of maintenance strategy that uses data analytics and machine learning algorithms to predict equipment failures before they occur.

Real-time analytics refers to the process of analyzing data as soon as it is generated or received, in order to make fast and informed decisions based on the insights gained. In manufacturing and automotive industries, real-time analytics is critical for optimizing processes, identifying potential issues before they escalate, and improving overall efficiency.

Servitization is the process by which a company shifts its focus from selling physical products to providing a comprehensive suite of services around those products.

Stream analytics is a type of data analysis that focuses on processing and analyzing data in real-time, as it is generated or "streamed" from various sources. The main goal of stream analytics is to extract valuable insights and patterns from these continuous data streams, allowing organizations to make faster and more informed decisions.

Streaming data refers to a continuous flow of data that is generated in real-time and constantly updated. This data is usually too large and too complex to be processed by traditional methods. Streaming data can come from various sources such as sensors, social media, websites, and other digital platforms.

Zero Trust is a security concept and model that requires all users, devices, and applications to be authenticated and authorized before they are allowed to access a network or resources within it.