ML model lifecycle in telecom: data -> training -> deployment -> monitoring

Data Collection and Feature Engineering

The ML lifecycle in telecom begins with data collection from diverse sources: RAN counters (CQI, RSRP, throughput), core KPIs (session setup success rate, handover latency), transport metrics (link utilization, BER), and subscriber data (usage patterns, location history). Raw telecom data requires significant preprocessing -- handling missing counters during maintenance windows, normalizing across vendor-specific formats, and engineering features that capture temporal patterns such as busy-hour traffic ratios or week-over-week trends. Data quality directly determines model accuracy, making…