Learn
Become an AI telecom engineer
Three paths, depending on where you're coming from. Every module is short (15–25 minutes), practical, and written by someone who's shipped this stuff against real operator data. Modules ship when they ship — start with the outline today.
RAN fundamentals for AI engineers
Everything a machine-learning engineer needs to be useful in a telecom optimisation team
For: ML / AI engineers entering telecom. Assumes Python + linear algebra; assumes nothing about cellular networks.
- →Why RAN data is different
- →Cell identifiers: NCI, ECGI, ECI, NCGI
- →PM counters — what they are, what they aren't
- →CM and the MO hierarchy
- + 8 more
AI engineering for telecom
How to ship LLM and ML systems into the operator workflow without breaking things
For: RAN engineers learning ML/AI. Assumes deep telco knowledge; assumes Python literacy.
- →Where LLMs belong in a RAN team
- →RAG for network knowledge
- →Detector design — heuristic to ML to LLM
- →Eval-driven detector development
- + 6 more
Multi-vendor integration patterns
What neither RAN training nor ML training teaches you — the seams between vendors
For: Engineers (any background) who have seen "Nokia NRCELL ≠ Ericsson NRCellDU" and want to never write that bridge twice.
- →MO mapping across vendors
- →Counter translation and KPI normalization
- →Alarm severity mapping (X.733 ↔ vendor)
- →O-RAN R1 and the SMO interface
- + 2 more