Edge Computing Job Market Trends

AI processing that occurs on edge devices, enabling real-time data analysis and decision-making without relying on cloud connectivity.

Serverless computing paradigm where code is executed at the edge of the network, closer to the end-user. Enables faster response times and reduced latency for web applications.

Concerns the unintended consequences or disruptions caused by AI systems, requiring careful management and mitigation strategies.

A distributed computing paradigm that brings computation and data storage closer to the data source. It aims to improve response times and save bandwidth.

Refers to a network of independent computers that work together to achieve a common goal, often used to improve performance, scalability, and reliability.

A series of computing modules by NVIDIA designed for AI, robotics, and embedded computing. Known for its powerful performance in edge computing and real-time processing tasks.

AI processing done locally on devices rather than relying on external server-based processing. This approach enhances privacy, reduces latency, and improves efficiency for real-time applications.