Evidence-Grounded Hybrid AI for Network Troubleshooting with Human-in-the-Loop Validation

An evidence-grounded hybrid AI system for network troubleshooting with human-in-the-loop validation.

An evidence-grounded hybrid AI system for network troubleshooting with human-in-the-loop validation. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want to understand networks and cloud systems: building or simulating the environment, measuring performance and comparing approaches.

The main parts are a simulated or deployed environment, the algorithm or mechanism being studied, performance measurement against baselines, and charts that compare the results. The reference build uses Docker, Python, Linux, and Prometheus, with a clear project structure, setup instructions and sample data so that it can be run and extended.