How to learn edge computing systematically?

In combination with the relevant background of the core knowledge required for edge computing that we have previously sorted out, you can systematically learn according to the phased systematic path, covering the whole process from foundation to practice:
1. Basic theoretical stage
First, master the core principles of the distributed system, understand the basic concepts such as CAP theorem and final consistency, be familiar with the edge exclusive communication protocols such as MQTT and CoAP, and build a theoretical cognitive framework of edge computing.
2. Advanced Technology Stack
Learn Linux system operation and maintenance, master lightweight container and edge K8s orchestration technologies such as Docker and K3s, understand edge network related knowledge such as 5G MEC and network slicing, and fill the gaps in hardware and system layer capabilities.
3. Security and Cloud Edge Collaboration
Build a knowledge system for edge security, master protection methods such as lightweight device authentication and end-to-end data encryption, understand the core mechanisms of task scheduling, federated learning, and data streaming in cloud edge collaboration, and connect the full chain logic of cloud edge end.
4. Practical implementation
Build a testing environment using hardware such as Raspberry Pi, participate in open source projects such as EdgeX Foundry and KubeEdge, complete practical projects in industrial quality inspection and edge AI inference scenarios, and accumulate real implementation experience.